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

Seemandhar Jain; Keshav Gupta; Manmohan Chandraker

cs.CVOct 6, 2026

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

Authors: Seemandhar Jain, Keshav Gupta, Manmohan Chandraker

Organizations: University of California, San Diego

Abstract

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.

Figures & tables

Figure 2 : Simplified SPLATIFY pipeline. A high-level view of reproduction, refinement, and interdisciplinary method discovery. Fig. 3 shows the detailed architecture.

Figure 3 : Splatify overview. Reproduction (left): a parsing agent structures papers into a CFG-grounded knowledge base; fork-aware dependency resolution recovers components via citation graphs and repository diffs; GoT synthesis generates code in topological order with PSNR-guided and VLM-driven refinement. Knowledge discovery and invention (right): the populated knowledge base enables compositional improvement, where agents compose techniques to surpass original results, and interdisciplinary method creation, where physical priors from outside 3DGS literature are retrieved to address novel scene types.

Paper	Scene	Reported				Expert Impl.				Splatify (Ours)
		PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	#GS ( $10^{6}$ )	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	#GS ( $10^{6}$ )	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	#GS ( $10^{6}$ )
DWTGS [ 39 ]	mipnerf360	19.75	0.590	0.343	–	17.92	0.508	0.471	1.21	18.68	0.510	0.374	1.641
Dec. Densification [ 24 ]	mipnerf360	27.79	0.826	0.214	1.469	26.85	0.792	0.286	1.44	27.17	0.820	0.261	0.650
BOGausS [ 43 ]	mipnerf360	27.54	0.804	0.251	0.32	27.25	0.801	0.276	0.81	27.34	0.809	0.263	0.440
Opti3DGS [ 17 ]	mipnerf360	27.09	0.809	0.237	1.327	27.51	0.816	0.221	6.18	27.71	0.841	0.209	1.406
CLoD-GS [ 11 ]	Truck	25.29	0.880	0.148	1.46	24.83	0.854	0.227	0.40	25.48	0.873	0.128	0.384

Table 1 : Splatify vs. reported results and human implementations on Set 1. All other baselines failed to produce trainable code for any paper. Full results in the supplementary.

Figure 4 : Qualitative comparison of Splatify against baselines on Set 1 papers. Rendered novel views from Splatify and expert human implementation. All baselines fail to produce trainable code; Splatify closely matches expert quality.

Metric	Paper2Code	AutoP2C	GPT-5.2	Gemini 3.1 Pro	DeepSeek 3.2	NERFIFY	SPLATIFY (Ours)
Imports Resolve	✓	×	✓	✓	✓	✓	✓
Compiles / Trainable	×	×	$\sim$	$\sim$	×	✓	✓
Training Stability	×	×	×	×	×	×	✓
Gaussian Invariants	×	×	×	×	×	×	✓
Converges to Paper Results	×	×	×	×	×	$\sim$	✓

Table 2 : Executability comparison. All baselines fail to produce trainable code.

\sim

denotes occasional success that never reaches training stability or paper-level results.

Paper	Scene	Original Repository				Splatify (Ours)
		PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	#GS ( $10^{6}$ )	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	#GS ( $10^{6}$ )
3DGS [ 28 ]	Bicycle	25.246	0.771	0.205	–	26.162	0.765	0.359	0.964
LP3DGS [ 66 ]	Bicycle	24.906	0.737	0.264	2.51	24.639	0.740	0.189	1.310
GNS [ 13 ]	Truck	26.520	0.899	0.119	0.60	25.970	0.891	0.095	0.490
Micro-Splatting [ 31 ]	Kitchen	34.100	0.960	0.011	0.67	33.145	0.935	0.099	1.620
CompGS [ 38 ]	Counter	28.467	0.895	0.222	0.385	28.283	0.894	0.153	0.665

Table 3 : Set 2: Splatify vs. original author repositories. Metrics from original papers compared against Splatify ’s automated gsplat implementations trained under identical conditions. Full results in the supplementary.

	Paper2Code					Gemini 3.1 Pro					DeepSeek 3.2					GPT-5.2 Thinking					Splatify
Paper	$C$ $\uparrow$	$I$ $\downarrow$	$M$ $\downarrow$	$W$ $\uparrow$	Score $\uparrow$	$C$ $\uparrow$	$I$ $\downarrow$	$M$ $\downarrow$	$W$ $\uparrow$	Score $\uparrow$	$C$ $\uparrow$	$I$ $\downarrow$	$M$ $\downarrow$	$W$ $\uparrow$	Score $\uparrow$	$C$ $\uparrow$	$I$ $\downarrow$	$M$ $\downarrow$	$W$ $\uparrow$	Score $\uparrow$	$C$ $\uparrow$	$I$ $\downarrow$	$M$ $\downarrow$	$W$ $\uparrow$	Score $\uparrow$
TrickGS [ 1 ]	0.35	0.30	0.35	0.40	0.41	0.50	0.20	0.30	0.50	0.51	0.65	0.15	0.20	0.60	0.64	0.60	0.15	0.25	0.65	0.62	0.90	0.05	0.05	0.92	0.94
2DGS [ 23 ]	0.85	0.08	0.07	0.50	0.55	0.55	0.15	0.30	0.40	0.40	0.80	0.10	0.10	0.55	0.58	0.75	0.10	0.15	0.60	0.61	1.00	0.00	0.00	0.95	0.95
Scaffold-GS [ 34 ]	0.35	0.20	0.45	0.60	0.34	0.45	0.10	0.45	0.45	0.44	0.40	0.35	0.25	0.40	0.42	0.60	0.25	0.15	0.55	0.59	1.00	0.00	0.00	1.00	1.00
GaussianShader [ 26 ]	0.45	0.30	0.25	0.40	0.40	0.55	0.28	0.17	0.45	0.50	0.60	0.22	0.18	0.48	0.58	0.72	0.15	0.13	0.62	0.73	1.00	0.00	0.00	0.95	1.00
Pixel-GS [ 67 ]	0.25	0.45	0.30	0.60	0.40	0.40	0.10	0.50	0.80	0.46	0.55	0.15	0.30	0.80	0.67	0.65	0.10	0.25	0.85	0.72	1.00	0.00	0.00	1.00	1.00

Table 4 : Novelty coverage on Set 3.

C

: correct,

I

: incorrect/partial,

M

: missing,

W

: hyperparameter fidelity, Score: weighted semantic score (0–1). Full results in the supplementary.

	Before				After
Paper	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	#GS	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	#GS	Things Added
Opti3DGS [ 17 ]	27.71	.841	.209	1.40	28.05	.853	.125	4.95	Adaptive densif. threshold + opacity entropy reg.
MSAA 3DGS [ 55 ]	25.35	.758	.369	2.73	26.19	.791	.293	2.96	Bilateral grid + losses
GNS [ 13 ]	25.97	.891	.095	0.49	26.15	.902	.097	0.61	AA rasterization + denser init + post-densif. losses
DWTGS [ 39 ]	18.68	.510	.374	1.64	21.09	.725	.281	2.43	Patch structure loss + opacity/normal/depth reg.
CompGS [ 38 ]	28.28	.894	.153	0.67	28.50	.899	.146	0.63	EMA centroid updates + grad-based codebook refine

Table 5 : Compositional discovery on Set 1, Set 2 papers. “Before” is the reproduction-only baseline; “After” is the best validated configuration found by the discovery pipeline. All numbers on same scene as Tables 1 and 3 .

Figure 5 : Cross-domain comparison. Ground truth, 3DGS, and agent-generated method for Nebula and Bubble. NebulaGS resolves opaque shell artifacts; BubbleGS captures iridescent color variation. Additional scenes in the supplementary.

Configuration	Score	Train.	C
		(%)
Splatify (Full)	0.97	100	1.00
Knowledge Sources:
w/o CFG Constraints (Stage 1)	0.91	70	0.85
w/o In-context Examples (Stage 1)	0.73	100	1.00
w/o Fork-aware Recovery (Stage 2)	0.64	100	0.70

Table 6 : Component ablation. Impact of each component on synthesis quality, averaged over 10 papers from SplatifyBench .

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ATSplat: Compact Feed-forward 3D Gaussian Splatting with Adaptive Token Expansion

In Cho, Jeonghwan Cho, Mijin Yoo, Gim Hee Lee+1

Yonsei University, South Korea · National University of Singapore, Singapore

Feed-Forward 3D Gaussian Splatting Feed-Forward 3D Gaussian Novel View Synthesis

Open paper AskPDF arXiv

3D Gaussian Splatting (3DGS) achieves high-quality novel-view synthesis by optimizing freely placed primitives in 3D and adaptively densifying them in under-reconstructed regions. However, this scene-adaptive capacity allocation is largely lost in existing feed-forward 3DGS methods, which commonly regress Gaussians at input pixels and lift them along camera rays. Such pixel-aligned formulations make the number and placement of primitives depend on image resolution and input viewpoints rather than scene complexity, resulting in dense and often redundant Gaussian sets. We present ATSplat, a feed-forward 3DGS framework that restores the adaptive allocation capability of 3DGS optimization through Adaptive 3D Tokens. ATSplat first lifts coarse patch-level depth and camera cues into sparse 3D anchor tokens, forming a compact scaffold of the scene. Each token is then regressed into local Gaussians with learnable 3D offsets, decoupling primitive placement from input image grids. An Adaptive Token Expansion module predicts a token-level uncertainty score, supervised by rendering error maps, and selectively expands high-uncertainty tokens through learnable expansion layers. This sparse-to-adaptive formulation enables ATSplat to concentrate primitives in challenging regions while maintaining a compact representation. Experiments on two representative datasets, RealEstate10K and DL3DV, show that ATSplat achieves state-of-the-art rendering quality while reducing the number of Gaussians by more than

5.7\times

compared with dense feed-forward 3DGS methods. From 12 input images at

512 \times 960

resolution, ATSplat completes reconstruction in less than a second using a single commercial GPU, and renders high-quality novel views at 1136 FPS (

512 \times 960

) with only 311K Gaussians.

Abstract

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