3DGS Training

3DGS: 3D Gaussian Splatting

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30 papers5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Oct 19, 2026

Rethinking Sparse-View 3D Gaussian Splatting: A Few-Shot Learning Perspective on Overfitting Prevention

Sparse-view novel view synthesis (NVS) is essential for AR/VR and real-time communication applications, yet remains challenging when reconstructing 3D scenes from limited viewpoints. While 3D Gaussian Splatting (3DGS) excels in dense-view scenarios, it suffers from overfitting and geometry degradation under sparse inputs. Current approaches often tackle these problems from a local perspective, addressing either data quality or training regularization individually, lacking a unified and interpretable framework for systematic improvement. We introduce a new perspective that reframes sparse-view 3DGS from a few-shot learning viewpoint, treating each Gaussian primitive as a neuron and leveraging deep learning regularization techniques. Under this unified framework, our approach generates more effective supervision from scarce observations through two strategies: (1) data-related supervision enrichment via optical flow-guided pseudo-view synthesis and joint camera-scene optimization; and (2) training-time regularization via motion-aware dropout and early stopping, which suppress overfitting while preserving dynamic-scene stability. Extensive experiments on the SIGGRAPH Asia 2025 volumetric video challenge sparse-view dataset demonstrate significant improvements over state-of-the-art optimization-based and feed-forward baselines. Our system won first place in the sparse-view track of the SIGGRAPH Asia 2025 challenge.

Lejia Ye, Bin Wu, Jingjing Gao +2

School of Aeronautics and Astronautics, Sun Yat-sen University, China

Novel View Synthesis Sparse-View 3D Reconstruction 3DGS Training Few-Shot Learning

Oct 19, 2026

Where Do the Gaussians Go? Foreground Attraction for Camera-Based Weakly Supervised 3D Occupancy

Camera-based 3D semantic occupancy models can be trained without 3D labels by rendering their predictions into the images and supervising them with 2D pseudo labels. In sparse Gaussian representations trained this way, Gaussian positions are learned only through pixel-wise rendering losses, which we hypothesize give small foreground objects little weight. In trained GaussianFlowOcc models, the Gaussian density on foreground voxels is on average only 1.62 times that on background voxels. We propose a training-only foreground attraction loss that back-projects foreground pseudo-label pixels with pseudo depth and pulls nearby Gaussians toward these 3D points, favoring Gaussians that already predict the target class; the inference network is unchanged. On Occ3D-nuScenes, the loss raises this concentration to 2.40 over three seeds. Over three seeds, the mean foreground IoU increases from 6.29 to 6.96 and is higher for each seed, whereas the change in mean mIoU (13.53 to 13.82) is within the seed-to-seed variation of the baseline. Single-run ablations further indicate that pseudo-label targets outperform targets placed at ground-truth voxels and that acting on the Gaussian positions alone is not sufficient, and a single-seed test shows that the model degrades less when fewer Gaussians are kept at inference.

Jeagon Song, Jhonghyun An

School of Computing, Gachon University, Seongnam 13120, Republic of Korea.

3D Occupancy Prediction 3D Scene Understanding 3DGS Training

Oct 14, 2026

InfiniSplat v2: Analyzing and Improving Feed-Forward 3DGS from Any Views

Feed-forward Gaussian reconstruction builds 3D scenes directly from input images for novel view rendering, but learning Gaussian attributes from predicted geometry for high-quality rendering under large viewpoint changes remains challenging. We analyze limitations in Gaussian attribute learning and introduce InfiniSplat v2, which improves rendering quality through architectural and training changes. Our method decouples geometry and attribute prediction with a frozen geometry branch, aligns predicted Gaussians using ground-truth camera poses during training, and adopts large-angle target view supervision. Across six datasets, the feed-forward outputs of the same trained model achieve state-of-the-art mean LPIPS and DISTS among the evaluated methods in both pose-conditioned and pose-free settings. In the pose-conditioned setting, mean LPIPS and DISTS decrease by 25.8% and 32.9%, respectively, compared with the strongest evaluated baselines. We further find that opacity learned with only image reconstruction supervision suppresses geometrically inconsistent points. Project page: https://zju3dv.github.io/InfiniSplat2/

Yongzhen Hu, Hao Yu, Jiawei Wang +7

Zhejiang University · Hello TransTech · Shenzhen University +1

3D Gaussian Splatting Feed-Forward 3D Reconstruction Novel View Synthesis 3DGS Training

Oct 6, 2026

SPLATIFY: Reproduce, Discover, Innovate! From Papers and Ideas to Trainable 3DGS Code

The rapid growth of 3D Gaussian Splatting (3DGS) research demands significant effort to reimplement papers before building on them. We introduce SPLATIFY, a multi-agent framework that converts 3DGS papers into trainable gsplat-based implementations, where generic paper-to-code methods and frontier models fail. SPLATIFY achieves this through five innovations: (1) A context-free grammar for gsplat over a modular method template with extension points for losses, densification, rendering, and optimization, constraining synthesis so generated code satisfies gsplat's architectural invariants by construction. (2) Architectural elements for faithful reproduction: fork-aware citation recovery retrieving component-level code at function-level granularity, Graph-of-Thought synthesis in topological dependency order, RAG-guided in-context example selection from over 20 verified implementations, and visual feedback combining PSNR-guided regeneration, Gaussian-level structural checks, and VLM-driven patching. (3) Knowledge-driven compositional improvement that autonomously finds weaknesses and composes complementary regularizers, losses, and densification strategies to improve upon original results. (4) Interdisciplinary method discovery where agents retrieve physical priors from outside the 3DGS literature and compose them with rendering knowledge to produce methods for previously unaddressed scene types. (5) SPLATIFY-Bench, an evaluation framework across 30 diverse 3DGS papers. On papers without public code, SPLATIFY matches expert implementations while reducing development time from weeks to minutes, and through compositional discovery further improves PSNR by up to 2.4 dB. We additionally demonstrate novel methods for volumetric nebula rendering and other scientific domains, synthesized entirely by SPLATIFY.

Seemandhar Jain, Keshav Gupta, Manmohan Chandraker

University of California, San Diego

Code Generation 3D Gaussian Splatting 3DGS Training Autonomous Scientific Discovery

Oct 2, 2026

Budgeted-GS: Real-Time Large-Scale Gaussian Splatting via Factoring LOD

3D Gaussian Splatting achieves excellent visual quality with real-time rendering, but at the scale of entire cities it does not fit: a trained model carries millions of primitives and gigabytes of memory, and real-time rendering at high quality on a consumer GPU remains out of reach. We introduce Budgeted-GS, a post-hoc method that turns any trained 3DGS model into a factoring tree, a multi-resolution hierarchy of moment-matched aggregates. After a construction pass of a few seconds, a single quality parameter selects, for each view, the level of detail that fits the memory of the target device, so the same city-scale model serves GPUs with widely different memory capacities. When a new scene is to be trained, the same theory applies: instead of growing a full-sized model and compressing it afterwards, budget-centered training first measures how many primitives the scene needs and then trains the model directly at that size, avoiding the wasted effort of optimizing primitives that are later discarded. Both methods are grounded in a measurable capacity floor, a budget-error law derived from optimal transport in phase space; selection rules certified by recent covering theorems decide which primitives are redundant. The floor answers how many primitives a scene actually needs and how many can safely be given up. We validate the floor on 13 public scenes under a preregistered protocol, and exercise both methods from object scenes to an official city capture, rendering it at native 1920x1080, full SH, in real time on one consumer GPU.

Haipeng Wang

Neusoft

3DGS Training 3D Gaussian Splatting 3DGS Acceleration 3DGS Compression

Sep 30, 2026

EffGS: Efficient and High-Fidelity Gaussian Splatting

3D Gaussian Splatting (3DGS) enables real-time novel view synthesis, but existing general-purpose acceleration methods suffer severe rendering quality degradation when extended to more complex, large-scale scenes. To address this issue, we propose EffGS, a more general acceleration framework that improves training and rendering efficiency while maintaining reconstruction quality comparable to or better than vanilla 3DGS across bounded and large-scale scenes. EffGS combines frequency-aware guidance, localized density control, and adaptive primitive scale modulation. First, an importance scoring mechanism combines pixel-wise reconstruction errors with a difference-of-Gaussians mask scheduled over training to provide stage-dependent spatial guidance. Second, localized densification and pruning restricts density modifications to Gaussians with valid projected footprints in the sampled views. Third, learnable per-Gaussian scale modulation adjusts effective primitive extent during optimization while retaining the Compact Box rasterization rule. Extensive experiments on bounded and large-scale scene datasets demonstrate a favorable balance between reconstruction quality, training time, and primitive count. Component ablations and matched-primitive-budget comparisons further support the effectiveness of the framework.

Changbai Li, Shuo Yang, Yichen Yang +2

Beihang University · Nanyang Technological University

3D Gaussian Splatting 3D Gaussian Pruning 3DGS Acceleration Adaptive Gaussian Densification Novel View Synthesis 3DGS Training

Sep 25, 2026

Gauss What You Need: Compact Gaussian Splatting Across Scene Scales

3D Gaussian Splatting reconstructs a scene as a collection of Gaussian primitives from a set of posed photographs called the capture. The number of primitives used to represent the scene affects reconstruction quality, storage, and rendering cost. How to select this number automatically across capture scales remains unresolved: configurations effective on standard benchmarks can leave larger captures with too few Gaussians to reconstruct fine details. We observe that the surface to represent, given by the capture's extent and resolution, is known before training, whereas its content complexity becomes apparent during training, through the reconstruction quality on the training views. We introduce TangoGS, which combines capture-derived model sizing with training-based adaptation: the capture determines the scale of the model, and training feedback determines its final size within that scale. Before training, TangoGS derives a learning allowance for model growth from the capture's total pixels after discounting views that re-observe the same scene points. During training, reconstruction quality guides how many Gaussians to add and remove. On 13 standard benchmark scenes, TangoGS matches the mean PSNR of the best-performing evaluated baseline, LeGS, with

48\%

fewer Gaussians. On eight large captures, the same configuration automatically scales to larger models when necessary, achieving the highest mean PSNR among evaluated methods:

0.54

dB above the runner-up with

2.3\times

as many Gaussians. Together, capture-derived learning allowances and training-quality guided density control enable a state-of-the-art quality--size compromise across scene scales without retuning.

Afif Boudaoud, Jiayi Liu, Alexandru Calotoiu +1

ETH Zürich

3D Gaussian Splatting 3DGS Training Adaptive Gaussian Densification 3DGS Compression 3D Gaussian Pruning

Sep 3, 2026

TruncGradGS: Improved 3D Gaussian Splatting via Truncated Gradient Updates

3D Gaussian Splatting has become a de facto scene representation for novel view synthesis, yet robustly learning 3D Gaussian primitives from visual input remains challenging. Standard optimization relies on gradient-based updates, but a common issue is the gradient vanishing phenomenon: a pixel far from a Gaussian primitive often has diminishing gradient magnitudes to influence primitive attributes, resulting in suboptimal scene reconstruction. In this paper, we propose a method to address gradient vanishing with a piecewise truncated gradient formulation that improves the optimization stability and robustness to initializations. We show that our method consistently improves 3D Gaussian Splatting with random and COLMAP initializations while being generalizable across static and dynamic Gaussian Splatting. As a by-product, we also examine the limitations of current benchmarks for dynamic scenes, and introduce a novel dataset for benchmarking dynamic Gaussian Splatting using synthetic 3D scenes. We demonstrate the effectiveness of our method in both static and dynamic settings for the public benchmarks and our proposed dataset.

Theo Morales, Nhat-Quynh Le-Pham, Robin Atkins +1

Trinity College Dublin · Dolby Laboratories

3D Gaussian Splatting Gradient Clipping Dynamic Scene Reconstruction 3DGS Training

Sep 3, 2026

Laplacian Frequency Hierarchies for Efficient 3D Gaussian Splatting Training

A key bottleneck in 3D Gaussian Splatting training is the continual growth of Gaussian primitives, which increases optimization cost and slows convergence, especially at high resolutions. We propose Laplacian Frequency Hierarchies, a simple yet efficient 3DGS scheme that combines Laplacian image decomposition with coarse-to-fine, frequency-staged training. After fitting lower-frequency structure, we archive the corresponding Gaussian field so that subsequent fields can optimize higher-frequency residuals without carrying the full primitive burden, and we compose the rendered components in the image domain via a Laplacian-style reconstruction at inference time. This design reduces the number of active Gaussians during training, thereby lowering optimization overhead and accelerating training. The proposed scheme is plug-and-play and orthogonal to prior 3DGS accelerations: it can be directly combined with strong backbones such as Taming-3DGS and FastGS to improve training speed with competitive reconstruction quality. It achieves average speedups of 1.73x and 1.21x at 1K setting, and 1.74x and 1.33x at 4K setting on Taming-3DGS and FastGS, with larger gains on more challenging scenes and increasingly pronounced benefits at higher resolutions.

Yixiong Yang, Sisheng Zhang, Qingsong Yan +2

Harbin Institute of Technology, Shenzhen, China · XGRIDS, China

3D Gaussian Splatting 3DGS Acceleration 3DGS Training

Aug 20, 2026

TopoGS: Topology-Aware Anchor Feature Aggregation for Large-Scale 3D Gaussian Splatting

Octree-based 3D Gaussian Splatting organizes anchors into multi-level hierarchies for level-of-detail rendering, but features at different levels are typically optimized independently, leaving the octree topology underused during feature learning. We observe that uniform cross-level aggregation produces asymmetric effects: fine-level anchors benefit from coarse context, whereas coarse-level anchors require selective information from their descendants. We therefore propose TopoGS, a topology-aware anchor feature aggregation framework with two lightweight components. Hierarchical Anchor Coupling establishes bidirectional cross-level gradient pathways by fusing per-level context triplets with a residual MLP. Structure-Aware Containment Aggregation uses octree containment and hash-based matching to distinguish anchors with valid parent-child relations from isolated anchors, then applies soft weighting to accommodate varying topological sparsity. Experiments on ten scenes from Mill19, UrbanScene3D, Tanks & Temples, MatrixCity, and WHU show consistent improvements over state-of-the-art methods. TopoGS achieves average PSNR gains of 2.13, 1.78, and 0.29 dB over the strongest reported baseline on aerial, ground-level, and synthetic-cartographic scenes, respectively, while rendering faster and using less memory. Code is available at https://github.com/WZ-CS/TopoGS.

Wei Zhang, Shiqiang Gong, Shengkai Yu +4

School of Computer Science, and with the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, P. R. China · School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, P. R. China · School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, P. R. China +1

3D Gaussian Splatting 3DGS Training Multi-Scale Feature Fusion

Aug 9, 2026

JSGS: JPEG State-Guided Supervision for 3D Gaussian Splatting from Mixed-Quality Views

Standard 3D Gaussian Splatting (3DGS) assumes that every input image faithfully samples scene radiance. However, mixed-quality JPEG images violate this assumption because compression-induced blocking and ringing artifacts can corrupt updates to Gaussians shared across views. To address this problem, we propose JPEG State-Guided Supervision for 3D Gaussian Splatting from Mixed-Quality Views (JSGS). JSGS uses luminance and chrominance quantization tables stored in each JPEG file to construct a view-specific JPEG observation operator. This operator encodes and decodes each rendered view for domain-matched comparison with the corresponding decoded input image. The luminance quantization table supplies continuous weights within a fixed middle frequency band. A loss in the low frequency band anchors coarse structure, while the weighted middle frequency loss redistributes supervision among the selected DCT coordinates. The resulting block disagreement also guides the Gaussian Controller to regularize small primitives with high opacity in disagreement regions. Across seven scenes and three mixed-quality schedules, JSGS achieves the lowest mean LPIPS and the highest mean SSIM under every schedule while rendering at approximately 150 FPS. Code: https://github.com/Jayden-Cui/JSGS.

Jinhua Cui, Anhong Wang, Kai Hu +5

1Taiyuan University of Science and Technology · 2Chn Energy Digital Intelligence Technology Development (Beijing)

3D Gaussian Splatting 3D Reconstruction 3DGS Training

Jul 20, 2026

Exploration Matters for Escaping the Blur Trap in 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) employs Gaussian primitives for explicit scene representation, facilitating real-time, high-fidelity reconstruction and novel view synthesis of complex scenes. However, the explicit modeling inherent in 3DGS introduces a gradient bias during optimization, rendering its non-convex optimization process highly susceptible to convergence toward local suboptimal solutions. This constitutes a fundamental limitation in 3DGS optimization, which we term the Blur Trap. To address this limitation, we integrate simple explicit exploration into the 3DGS optimization framework. First, through rigorous mathematical analysis of the 3DGS optimization formulation, we identify the underlying optimization bias responsible for the Blur Trap and categorize it into two distinct subtypes: the Far-Side Blur Trap and the Near-Side Blur Trap. Subsequently, we propose two highly straightforward exploration strategies (Random Seeding and Random Splitting) to mitigate the far-side and near-side blur traps, respectively. Experimental validation demonstrates that the incorporation of these exploration operators effectively and complementarily overcome the Blur Trap, achieving high-quality rendering performance across multiple datasets. Project page: https://chengbo-wang.github.io/ExploreGS/

Chengbo Wang, Guozheng Ma, Jinhong Wu +2

1Hunan University · 2Nanyang Technological University

3D Gaussian Splatting 3D Reconstruction 3DGS Training Novel View Synthesis

Jul 18, 2026

SPARE-GS: Structural Parsimony and Resource Efficiency for 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) achieves high-fidelity novel view synthesis in real-time; however its training efficiency and representation compactness are hindered by excessive primitive proliferation. To address this challenge, we formulate the structural evolution of 3DGS as a global budget-constrained optimization problem and derive an optimality condition, which requires the marginal utility of structural resources to be balanced across spatial regions under a finite primitive budget. Based on this formulation, we propose SPARE-GS, a general plug-and-play framework that dynamically aligns the distribution of 3D Gaussian primitives with regional representational demand. SPARE-GS estimates capacity-normalized regional demand, assigns adaptive target quotas, and uses regional budget deviations to coordinate densification, pruning and adaptive termination toward a more balanced structural allocation. Extensive experiments across standard, accelerated, and structure-enhanced 3DGS pipelines demonstrate that SPARE-GS reduces the Gaussian count and training time by an average of 30.38% and 23.81%, respectively, while improving the average PSNR. Moreover, the resulting compact representations reduce downstream processing time and improve the rate-distortion performance of diverse compression and pruning methods, demonstrating the broad applicability of global structural budget regulation.

Zhang Chen, Shuai Wan, Fuzheng Yang +3

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China · Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China · School of Telecommunication Engineering, Xidian University, Xi’an 710071, China +2

3D Gaussian Splatting 3D Gaussian Pruning Adaptive Gaussian Densification 3DGS Training

Jul 12, 2026

DP-Splat: Bayesian Nonparametric Complexity Control for Gaussian Splatting

3D Gaussian Splatting represents scenes as finite mixtures of anisotropic Gaussians whose number of components

K

is set by heuristic density control or user caps. Variational Bayes Gaussian Splatting (VBGS) recast splat fitting as conjugate variational inference, but

K

remains fixed. We replace the finite symmetric Dirichlet over mixture weights with a truncated stick-breaking Dirichlet-process prior -- and, as a theory-backed alternative, a sparse overfitted finite Dirichlet -- so that the number of occupied components adapts to the data while every update remains a closed-form coordinate-ascent step; a natural-gradient stochastic variant makes the per-step cost independent of the number of points. We give an exact monotonicity guarantee, a rigorous truncation-error bound correcting an anti-conservative large-

α

approximation in common use, and an honest account of what the fitted number of components estimates. Empirically: (i) the effective complexity

\hat{K}

adapts to scene complexity and recovers the true

K

within

\pm 1

on well-separated synthetic data with regime-appropriate concentration; (ii) a deconfounded comparison shows the DP prior's contribution is complexity selection, not per-component efficiency -- converged DP fits exceed single-pass fixed-

K

VBGS by +2.7 dB at matched budgets yet tie an equally converged fixed-

K

baseline, and on 3D scenes DP-Splat matches or exceeds VBGS's held-out color prediction with 5.9-7.6x fewer components; (iii) the posterior-predictive color variance is well calibrated on model-matched synthetic data; and (iv) the ordering suggested by exact-posterior asymptotics reverses under mean-field coordinate ascent: the DP prior resists over-splitting while the sparse finite mixture saturates its truncation, a gap between variational practice and posterior asymptotics documented across three orders of magnitude in

N

.

Aqi Dong

Embry-Riddle Aeronautical University

Bayesian Mixture Models Dirichlet Process Mixture Models Variational Inference 3D Gaussian Splatting Gaussian Mixture Models Bayesian Inference+1

Jul 6, 2026

Rendering-Aware Bayesian 3D Gaussian Splatting with Native Uncertainty and Adaptive Complexity Control

3D Gaussian splatting (3DGS) is a strong representation for real-time novel-view synthesis, but its standard training pipeline relies on point estimates and hand-tuned heuristics, providing no native uncertainty or principled complexity control. This is most limiting under sparse views or fixed acquisition budgets, where a model must identify weakly supported geometry and select informative views. We introduce a rendering-aware Bayesian 3DGS framework that tracks Gaussian geometry with a Normal-Inverse-Wishart posterior over means and covariances using renderer-derived surrogate summaries. An optional Dirichlet-process extension adds a probabilistic component-usage signal, and the training schedule makes the closed-form versus approximate inference boundary explicit. Re-rendering posterior geometry samples yields native predictive uncertainty for interval calibration and active view selection. In a fixed-budget 16-to-32 active-view task, native NIW acquisition improves PSNR by +0.453 dB and LPIPS by -0.0146 over a scoring-only 3-member standard-ensemble baseline, winning 29/39 scene-seed pairs and 10/13 scene means; it also improves over PPU-style (+0.355 dB) and NIW-proxy (+0.401 dB) acquisition. NIW native intervals reduce 95% coverage error by about 17x relative to a shared proxy (0.046 vs. 0.796) and are about 10x closer to nominal coverage than a 3-member deep ensemble (0.047 vs. 0.454) at roughly one-third the training cost. As a reconstruction compatibility check, paired NIW-vs-standard analysis over 39 scene-seed runs yields +0.030 dB PSNR with 1.6% additional training time. These results position Bayesian 3DGS as a practical probabilistic scene representation for decision-facing tasks such as active view selection.

Gaoxiang Jia, Vikram Appia, Junzhou Huang +1

Advanced Micro Devices, Inc. · Dept. of Computer Science and Engineering University of Texas at Arlington · Dept. of Mathematics & Division of Data Science University of Texas at Arlington

Active View Selection Active Perception Uncertainty Quantification 3D Gaussian Splatting Sparse-View 3D Reconstruction 3DGS Training+1

May 27, 2026

Eulerian Gaussian Splatting using Hashed Probability Pyramids

We introduce a probabilistic splat-based radiance field framework that retains the fast rasterization and test-time efficiency of 3D Gaussian Splatting (3DGS) while replacing heuristic primitive manipulation with gradient-based optimization of a volumetric probability density. Rather than relocating, splitting, or culling Gaussians via hand-tuned densification (e.g., ADC), we treat primitive locations as samples drawn from a persistent, learnable density. We instantiate this density using a novel, memory-efficient multi-scale hierarchical grid that enables end-to-end gradient-based optimization. To stabilize the optimization, we derive an unbiased gradient estimator with control variates that markedly reduces variance. By allowing probability mass to flow to where the loss demands, our framework eliminates brittle priors and naturally explores the volume, achieving state-of-the-art reconstruction quality on mip-NeRF 360 while preserving 3DGS-level rendering speed.

Mia Gaia Polansky, George Kopanas, Stephan Garbin +2

Harvard University · Google DeepMind · Google

3D Gaussian Splatting Neural Rendering Multi-View 3D Reconstruction Gaussian Splatting 3DGS Training

May 21, 2026

ForeSplat: Optimization-Aware Foresight for Feed-Forward 3D Gaussian Splatting

Feed-forward 3D Gaussian Splatting models offer fast single-pass reconstruction,but scaling them to match per-scene optimization quality is fundamentally hindered by the scarcity of large-scale 3D annotations. A practical compromise is predict-then-refine,where post-prediction optimization compensates for the limited capacity of the feed-forward network. However,standard feed-forward 3DGS is trained solely for zero-step rendering error,ignoring whether its output constitutes a good initialization for the downstream optimizer. We present ForeSplat,an optimization-aware training framework that equips feed-forward 3DGS models to produce initializations explicitly designed for rapid,effective refinement. By offloading part of the scene-modeling burden to the optimizer,ForeSplat substantially reduces the capacity pressure on the feed-forward model,making high-quality reconstruction feasible even with compact networks. At its core is MetaGrad,a lightweight multi-anchor meta-gradient training rule that bypasses costly higher-order differentiation through the 3DGS optimizer. MetaGrad unrolls a short inner-loop refinement trajectory,samples anchor states,and back-propagates aggregated first-order gradients to the prediction head as a surrogate optimization-aware signal. This fine-tuning adds no inference cost and enables high-quality reconstruction within seconds after a few refinement steps. We instantiate ForeSplat on diverse backbones,including AnySplat,Pi3X,and a distilled variant tailored for edge deployment. Across all tested architectures,a ForeSplat-trained initialization converges in fewer refinement steps and reaches a higher peak reconstruction quality than its vanilla counterpart,even fully converged. The framework consistently bridges the gap between amortized prediction and per-scene optimization,establishing a practical path toward lightweight,high-fidelity 3D reconstruction.

Yuke Li, Weihang Liu, Cheng Zhang +8

ShanghaiTech University · GGU Technology Co., Ltd · Stereye

3D Gaussian Splatting Meta-Learning 3D Reconstruction Feed-Forward 3D Reconstruction 3DGS Training

May 19, 2026

TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization

Training 3D Gaussian Splatting (3DGS) at billion-primitive scale is fundamentally memory-bound: each Gaussian primitive carries a large attribute vector, and the aggregate parameter table quickly exceeds GPU capacity, limiting prior systems to tens of millions of Gaussians on commodity single-GPU hardware. We observe that 3DGS training is inherently sparse and trajectory-conditioned: each iteration activates only the Gaussians visible from the current camera batch, so GPU memory can serve as a working-set cache rather than a persistent parameter store. Building on this insight, we introduce TideGS, an out-of-core training framework that manages parameters across an SSD-CPU-GPU hierarchy via three synergistic techniques: block-virtualized geometry for SSD-aligned spatial locality, a hierarchical asynchronous pipeline to overlap I/O with computation, and trajectory-adaptive differential streaming that transfers only incremental working-set deltas between iterations. Experiments show that TideGS enables training with over one billion Gaussians on a single 24 GB GPU while achieving the best reconstruction quality among evaluated single-GPU baselines on large-scale scenes, scaling beyond prior out-of-core baselines (e.g., approximately 100M Gaussians) and standard in-memory training (e.g., approximately 11M Gaussians).

Chonghao Zhong, Linfeng Shi, Hua Chen +4

Hong Kong University of Science and Technology · Great Wall Motor · Tsinghua University +1

3D Gaussian Splatting 3D Scene Representation 3DGS Training

May 15, 2026

Learn2Splat: Extending the Horizon of Learned 3DGS Optimization

3D Gaussian Splatting (3DGS) optimization is most commonly performed using general-purpose first-order optimizers such as Adam or SGD. Although robust across scenes, they update each parameter independently without exploiting the structural and spatial relationships among Gaussians, which slows convergence. Recent works introduced learned optimizers that predict correlated updates informed by inter-parameter and inter-Gaussian dependencies. However, those are trained for a fixed number of optimization iterations and rely on manually scheduled learning rates to avoid degradation. In this paper, we introduce Learn2Splat, a learned optimizer for 3DGS that avoids degradation over extended optimization horizons without auxiliary mechanisms. To enable this, we propose a meta-learning scheme that extends the optimization horizon via a checkpoint buffer and an optimizer rollout strategy, combined with an architecture that encodes gradient scale information in its latent states. Results show higher novel view synthesis quality at equal wall-clock time, while remaining stable over long horizons, with zero-shot generalization to unseen datasets and settings. To support our findings, we build a unified framework to train and evaluate learned and standard optimizers across sparse and dense view settings. Code and models will be released publicly. Our project page is available at https://autonomousvision.github.io/learn2splat .

Naama Pearl, Stefano Esposito, Haofei Xu +6

University of Tübingen, Tübingen AI Center · ETH Zurich · Meta Reality Labs

3D Gaussian Splatting Meta-Learning Learning to Optimize Novel View Synthesis 3DGS Training

May 9, 2026

ReorgGS: Equivalent Distribution Reorganization for 3D Gaussian Splatting

A converged 3D Gaussian Splatting (3DGS) model may approximate the target scene while remaining poorly parameterized for further optimization. We identify this failure mode as parameterization degeneration: high-opacity floaters attenuate gradients to true surfaces through alpha compositing, and redundant overlapping clusters create strongly coupled parameter blocks with nearly collinear Jacobian responses. These effects explain why continued optimization can plateau even when the model still contains removable artifacts. We propose ReorgGS, an equivalent distribution reorganization method for converged 3DGS models. ReorgGS treats the existing Gaussian set as an empirical probability field, resamples centers from it, estimates local anisotropic covariances with kNN, initializes low opacity, and continues optimization with the original 3DGS renderer and loss. Unlike opacity reset, which only rescales opacity on the old overlap graph, ReorgGS rebuilds centers, covariances, and visibility structure, thereby changing the graph itself. Our analysis shows that distributional equivalence is not optimization equivalence. The reorganized model preserves scene support while improving gradient accessibility under alpha compositing and reducing opacity-weighted overlap, thereby weakening local parameter coupling during subsequent optimization. Under the same additional optimization budget, ReorgGS improves fitting quality at a fixed Gaussian count, suppresses persistent floaters, and reduces rendering overhead from redundant overlap.

Luchao Wang, Kaimin Liao, Qian Ren +3

3D Gaussian Splatting 3D Reconstruction 3DGS Training 3D Scene Representation

May 7, 2026

AdpSplit: Error-Driven Adaptive Splitting for Faster Geometry Discovery in 3D Gaussian Splatting

Adaptive density control in 3D Gaussian Splatting (3DGS) repeatedly grows the Gaussian population through fixed-cardinality random splitting to discover useful scene structure. However, in vanilla 3DGS, its binary split operator requires many densification rounds to expose fine details, making it a bottleneck for efficient training schedules with fewer iterations. We introduce AdpSplit, an error-driven adaptive split operator that determines the number of split children and initializes the child parameters from L1-pixel-error region statistics, enabling fewer densification iterations, thus reduced training time, while preserving the rendering quality of full-schedule training. Across the MipNeRF360, Deep-Blending, and Tanks&Temples datasets, AdpSplit reduces the training time of multiple accelerated 3DGS pipelines by 9.2%-22.3% as a simple drop-in replacement for the standard split operator. With FastGS, AdpSplit matches the full-schedule PSNR on MipNeRF360 while reducing training time by 16.4%, corresponding to a 12.6x acceleration over vanilla 3DGS.

Yongjae Lee, Jingxing Li, Abhay Kumar Yadav +2

Arizona State University · Johns Hopkins University

3D Gaussian Splatting 3DGS Acceleration 3DGS Training Adaptive Gaussian Densification

Apr 30, 2026

VkSplat: High-Performance 3DGS Training in Vulkan Compute

We present VkSplat, a high-performance, cross-vendor 3D Gaussian Splatting (3DGS) training pipeline implemented fully in Vulkan compute, addressing performance and compatibility limitation of existing training pipelines. With various optimizations, we achieve

3.3\times

speed and

33\%

VRAM reduction over CUDA+PyTorch baseline, maintaining quality, and demonstrating compatibility across GPU vendors. To the best of our knowledge, this is the first fully-Vulkan-based 3DGS training pipeline that achieves state-of-the-art performance. Code: https://github.com/harry7557558/vksplat

Jingxiang Chen, Mohamed Ibrahim, Yang Liu

Huawei Canada

3D Gaussian Splatting 3DGS Acceleration GPU Kernel Optimization GPU Acceleration High-Performance Computing 3DGS Training

Mar 23, 2026

Drop-In Perceptual Optimization for 3D Gaussian Splatting

Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings. To address this, we systematically explore perceptual optimization strategies for 3DGS by searching over a diverse set of distortion losses. We conduct the first-of-its-kind large-scale human subjective study on 3DGS, involving 39,320 pairwise ratings across several datasets and 3DGS frameworks. A regularized version of Wasserstein Distortion, which we call WD-R, emerges as the clear winner, excelling at recovering fine textures without incurring a higher splat count. WD-R is preferred by raters more than

2.3\times

over the original 3DGS loss, and

1.5\times

over the current best method Perceptual-GS. WD-R also consistently achieves state-of-the-art LPIPS, DISTS, and FID scores across various datasets, and generalizes across recent frameworks, such as Mip-Splatting and Scaffold-GS, where replacing the original loss with WD-R consistently enhances perceptual quality within a similar resource budget (number of splats for Mip-Splatting, model size for Scaffold-GS), and leads to reconstructions being preferred by human raters

1.8\times

and

3.6\times

, respectively. We also find that this carries over to the task of 3DGS scene compression, with

\approx 50\%

bitrate savings for comparable perceptual metric performance.

Ezgi Ozyilkan, Zhiqi Chen, Oren Rippel +2

Apple · New York University Tandon School of Engineering

3D Gaussian Splatting Perceptual Loss Wasserstein Distance 3DGS Training

Mar 21, 2026

The Role of Initialization in 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) has become the method of choice for photo-realistic novel view synthesis (NVS), due to its efficiency and compelling visual quality. 3DGS represents the scene as a set of 3D Gaussians, parameterized by their position, spatial extent, and view-dependent color. Starting from an initial point cloud, 3DGS refines the Gaussians' parameters to reconstruct a set of training images as accurately as possible. Typically, a sparse Structure-from-Motion point cloud is used as initialization. Thus, in order to obtain a full scene representation, 3DGS methods rely on a densification stage. In this paper, we systematically study how initialization affects 3DGS NVS performance and geometric quality, using several densification strategies. We show that dense initialization does not lead to consistent visual improvements when paired with strong densification. Despite that, it helps in generalization to off-trajectory views and significantly improves geometric accuracy of the scenes. Our code is available at https://github.com/deivse/ivd_splat.

Ivan Desiatov, Torsten Sattler

Czech Technical University in Prague, Faculty of Electrical Engineering · Czech Technical University in Prague, Czech Institute of Informatics, Robotics and Cybernetics

3DGS Training 3D Gaussian Splatting Novel View Synthesis 3D Reconstruction

Mar 19, 2026

Matryoshka Gaussian Splatting

The ability to render scenes at adjustable fidelity from a single model, known as level of detail (LoD), is crucial for practical deployment of 3D Gaussian Splatting (3DGS). Existing discrete LoD methods expose only a limited set of operating points, while concurrent continuous LoD approaches enable smoother scaling but often suffer noticeable quality degradation at full capacity, making LoD a costly design decision. We introduce Matryoshka Gaussian Splatting (MGS), a training framework that enables continuous LoD for standard 3DGS pipelines without sacrificing full-capacity rendering quality. MGS learns a single ordered set of Gaussians such that rendering any prefix, the first k splats, produces a coherent reconstruction whose fidelity improves smoothly with increasing budget. Our key idea is stochastic budget training: each iteration samples a random splat budget and optimises both the corresponding prefix and the full set. This strategy requires only two forward passes and introduces no architectural modifications. Experiments across four benchmarks and six baselines show that MGS matches the full-capacity performance of its backbone while enabling a continuous speed-quality trade-off from a single model. Extensive ablations on ordering strategies, training objectives, and model capacity further validate the designs.

Zhilin Guo, Boqiao Zhang, Hakan Aktas +11

University of Cambridge · Chalmers University of Technology · Google

3D Gaussian Splatting 3DGS Training 3DGS Acceleration Matryoshka Representation Learning

Mar 10, 2026

Speeding Up the Learning of 3D Gaussians with Much Shorter Gaussian Lists

3D Gaussian splatting (3DGS) has become a vital tool for learning a radiance field from multiple posed images. Although 3DGS shows great advantages over NeRF in terms of rendering quality and efficiency, it remains a research challenge to further improve the efficiency of learning 3D Gaussians. To overcome this challenge, we propose novel training strategies and losses to shorten each Gaussian list used to render a pixel, which speeds up the splatting by involving fewer Gaussians along a ray. Specifically, we shrink the size of each Gaussian by resetting their scales regularly, encouraging smaller Gaussians to cover fewer nearby pixels, which shortens the Gaussian lists of pixels. Additionally, we introduce an entropy constraint on the alpha blending procedure to sharpen the weight distribution of Gaussians along each ray, which drives dominant weights larger while making minor weights smaller. As a result, each Gaussian becomes more focused on the pixels where it is dominant, which reduces its impact on nearby pixels, leading to even shorter Gaussian lists. Eventually, we integrate our method into a rendering resolution scheduler which further improves efficiency through progressive resolution increase. We evaluate our method by comparing it with state-of-the-art methods on widely used benchmarks. Our results show significant advantages over others in efficiency without sacrificing rendering quality.

Jiaqi Liu, Zhizhong Han

Machine Perception Lab, Wayne State University, Detroit, USA

3DGS Training 3D Gaussian Splatting 3DGS Acceleration

Mar 9, 2026

SkipGS: Post-Densification Backward Skipping for Efficient 3DGS Training

3D Gaussian Splatting (3DGS) achieves real-time novel-view synthesis by optimizing millions of anisotropic Gaussians, yet its training remains expensive, with the backward pass dominating runtime in the post-densification refinement phase. We observe substantial update redundancy in this phase: many sampled views have near-plateaued losses and provide diminishing gradient benefits, but standard training still runs full backpropagation. We propose SkipGS with a novel view-adaptive backward gating mechanism for efficient post-densification training. SkipGS always performs the forward pass to update per-view loss statistics, and selectively skips backward passes when the sampled view's loss is consistent with its recent per-view baseline, while enforcing a minimum backward budget for stable optimization. On Mip-NeRF 360, compared to 3DGS, SkipGS reduces end-to-end training time by 23.1%, driven by a 42.0% reduction in post-densification time, with comparable reconstruction quality. Because it only changes when to backpropagate without modifying the renderer, representation, or loss, SkipGS is plug-and-play and compatible with other complementary efficiency strategies, enabling additive speedups. Code is available at https://github.com/ASU-ESIC-FAN-Lab/SkipGS.

Jingxing Li, Yongjae Lee, Deliang Fan

Arizona State University, Tempe, AZ 85281, USA

3D Gaussian Splatting 3DGS Acceleration Neural Rendering Novel View Synthesis 3DGS Training

Mar 9, 2026

Improving Continual Learning for Gaussian Splatting based Environments Reconstruction on Commercial Off-the-Shelf Edge Devices

Novel view synthesis (NVS) is increasingly relevant for edge robotics, where compact and incrementally updatable 3D scene models are needed for SLAM, navigation, and inspection under tight memory and latency budgets. Variational Bayesian Gaussian Splatting (VBGS) enables replay-free continual updates for the 3DGS algorithm by maintaining a probabilistic scene model, but its high-precision computations and large intermediate tensors make on-device training impractical. We present a precision-adaptive optimization framework that enables VBGS training on resource-constrained hardware without altering its variational formulation. We (i) profile VBGS to identify memory/latency hotspots, (ii) fuse memory-dominant kernels to reduce materialized intermediate tensors, and (iii) automatically assign operation-level precisions via a mixed-precision search with bounded relative error. Across the Blender, Habitat, and Replica datasets, our optimised pipeline reduces peak memory from 9.44 GB to 1.11 GB and training time from ~234 min to ~61 min on an A5000 GPU, while preserving (and in some cases improving) reconstruction quality of the state-of-the-art VBGS baseline. We also enable for the first time NVS training on a commercial embedded platform, the Jetson Orin Nano, reducing per-frame latency by 19x compared to 3DGS.

Ivan Zaino, Matteo Risso, Daniele Jahier Pagliari +3

Politecnico di Torino · VERSES

3DGS Training 3D Gaussian Splatting Novel View Synthesis Deep Learning Optimization GPU Kernel Optimization Edge Computing

Dec 19, 2025

G3Splat: Geometrically Consistent Generalizable Gaussian Splatting

3D Gaussians have become a powerful scene representation for real-time splatting and high-quality novel-view synthesis. This has motivated generalizable splatting -- methods that adapt feed-forward geometry prediction networks to produce per-pixel Gaussians from a set of images. However, most generalizable splatting pipelines are supervised primarily through a view-synthesis loss to predict Gaussian orientation, anisotropic scale, opacity, and appearance in addition to their locations. We show that this learning objective is under-constrained. Models trained with view synthesis alone produce splats whose orientations and scales have no geometric connotation. The result is that, while producing decent view-synthesis performance, nearly all generalizable splatting methods produce geometrically inaccurate and misaligned Gaussians. We introduce G3Splat, a geometry-consistent generalizable splatting framework that addresses these degeneracies through differentiable geometric priors on the predicted 3D Gaussians, making the learning problem well-posed. These priors encourage the per-pixel splats to remain on their viewing rays and to orient themselves in accordance with local surfaces. Our priors are architecture-agnostic and can be incorporated into any previously studied geometric backbone for generalizable splatting, as well as different scene representations. We test G3Splat with both DUSt3R-style and VGGT-style backbones to predict pixel-aligned full-rank 3DGS as well as surfel-like 2DGS. Trained on RE10K, G3Splat produces Gaussian splats with significantly higher geometric fidelity than baselines, providing state-of-the-art novel-view depth, mesh reconstruction, and relative pose estimation performance while preserving novel-view synthesis quality, as evaluated on datasets such as ACID and ScanNet. Code and pretrained models are released on our project page.

Mehdi Hosseinzadeh, Shin-Fang Chng, Yi Xu +3

Australian Institute for Machine Learning · Goertek Alpha Labs · MBZUAI

3D Gaussian Splatting 3D Reconstruction Novel View Synthesis Gaussian Splatting 3DGS Training

Apr 29, 2025

GaussTrap: Stealthy Backdoor Attacks on 3D Gaussian Splatting for Targeted Scene Misperception

3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for real-time scene representation and novel view synthesis, gaining traction in safety-critical applications such as autonomous driving and AR/VR systems. However, the security vulnerabilities of 3DGS remain largely unexplored. In this paper, we conduct a comprehensive study of backdoor threats in 3DGS pipelines. We uncover an inherent security vulnerability in the adaptive density control mechanism of 3DGS, originally designed for detailed reconstruction, which enables attackers to induce localized gaussian proliferation, effectively embedding malicious backdoors into the scene geometry while remaining invisible from other viewpoints. To expose this threat, we propose GaussTrap, a novel stealthy poisoning framework tailored for 3DGS. GaussTrap operates in three stages (adversarial injection, geometric stabilization, and global fidelity restoration) to embed imperceptible yet adversarial Gaussian components into the reconstruction. It ensures consistent poisoning effects at specific viewpoints while preserving photorealism in benign views, thereby achieving high attack efficacy with low detectability. Extensive experiments on both synthetic and real-world benchmarks demonstrate that GaussTrap reliably induces targeted visual corruption under trigger conditions while maintaining high rendering fidelity elsewhere, highlighting a critical security blind spot in modern 3D rendering systems. The code is available at https://github.com/acang425/GaussTrap.

Jiaxin Hong, Sixu Chen, Shuoyang Sun +5

Harbin Institute of Technology(Shenzhen) · South China University of Technology · Shenzhen Internation Graduate School, Tsinghua University +1

3D Gaussian Splatting 3DGS Training 3D Scene Representation