AirSplat: Alignment and Rating for Robust Feed-Forward 3D Gaussian Splatting

Minh-Quan Viet Bui; Jaeho Moon; Munchurl Kim

cs.CVMar 26, 2026

AirSplat: Alignment and Rating for Robust Feed-Forward 3D Gaussian Splatting

Authors: Minh-Quan Viet Bui, Jaeho Moon, Munchurl Kim

Organizations: KAIST, Republic of Korea

Abstract

While 3D Vision Foundation Models (3DVFMs) have demonstrated remarkable zero-shot capabilities in visual geometry estimation, their direct application to generalizable novel view synthesis (NVS) remains challenging. In this paper, we propose AirSplat, a novel training framework that effectively adapts the robust geometric priors of 3DVFMs into high-fidelity, pose-free NVS. Our approach introduces two key technical contributions: (1) Self-Consistent Pose Alignment (SCPA), a training-time feedback loop that ensures pixel-aligned supervision to resolve pose-geometry discrepancy; and (2) Rating-based Opacity Matching (ROM), which leverages the local 3D geometry consistency knowledge from a sparse-view NVS teacher model to filter out degraded primitives. Experimental results on large-scale benchmarks demonstrate that our method significantly outperforms state-of-the-art pose-free NVS approaches in reconstruction quality. Our AirSplat highlights the potential of adapting 3DVFMs to enable simultaneous visual geometry estimation and high-quality view synthesis.

Figures & tables

Figure 1: (a) Our proposed AirSplat adapts 3D-VS-VFMs using Self-Consistent Pose Alignment (SCPA) and Rating-based Opacity Matching (ROM) to resolve inherent pose-geometry discrepancies and multi-view inconsistencies. Compared to (b) baseline DA3 [ 22 ] which shows floaters (red boxes) and blurred regions (dashed yellow boxes), (c) AirSplat eliminates these artifacts, faithfully recovering the scene structure shown in (d) the ground truth.

Figure 2: Overview of our AirSplat training pipeline. During training of a Gaussian head of a 3DVFM, the encoder and its pose head are frozen. Our SCPA module corrects the predicted target pose to align the rendered target views to the GT target views. Our ROM module gathers the geometric feedback from the teacher model, and based on the feedback, it enhances the multi-view consistency of the predicted 3D primitives.

Figure 3: Comparison of training paradigms in pose-free NVS. (a) Training with the context-only strategy, adopted in [ 16 ] , leads to a lack of direct supervision for novel viewpoints. (b) The context-target strategy, following [ 14 ] , results in spatial misalignment. (c) Our SCPA corrects the inherent spatial drift, enabling the network to learn both structurally consistent 3D geometry and robust novel view synthesis.

Figure 4: Effect of Self-Consistent Pose Alignment (SCPA). We compare rendered target views using the initial predicted pose $\hat{\bm{P}}^{(1)}_{\text{tgt},t}$ , our aligned pose $\hat{\bm{P}}_{\text{tgt},t}^{\text{align}}$ against the ground truth, during training. As highlighted by the red arrows, the initial pose prediction results in a noticeable spatial shift, evident in the misaligned structural lines on the ground. Our proposed SCPA corrects the inherent spatial drift and ensures that the model learn structurally consistent 3D geometry and robust NVS. In the error maps, blue indicates small errors, and red indicates large errors.

Methods		12 Views			24 Views			36 Views
Methods		PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
w/ Pose	MonoSplat [ 25 ] (CVPR25)	18.16	0.663	0.336	16.66	0.593	0.391	15.79	0.551	0.424
	MVSplat [ 8 ] (ECCV24)	17.98	0.638	0.357	17.27	0.609	0.379	OOM	OOM	OOM
	DepthSplat [ 56 ] (CVPR25)	22.56	0.793	0.200	21.00	0.734	0.248	19.60	0.676	0.296
Pose- free	NoPoSplat [ 59 ] (ICLR25)	17.15	0.571	0.437	17.10	0.570	0.443	17.09	0.570	0.446
	AnySplat [ 16 ] (SIGGRAPHAsia25)	18.69	0.591	0.273	19.15	0.610	0.257	19.31	0.615	0.251

Table 1: Quantitative comparison of NVS performance on the RE10K dataset [ 66 ] under various input-view settings. Bold indicates the best performance. OOM indicates out-of-memory inference error.

Figure 5: Qualitative comparison of NVS performance on RE10K dataset [ 66 ] .

Methods		12 Views			24 Views			36 Views
Methods		PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
w/ Pose	MVSplat [ 8 ] (ECCV24)	21.55	0.729	0.239	OOM	OOM	OOM	OOM	OOM	OOM
w/ Pose	DepthSplat [ 56 ] (CVPR25)	22.14	0.725	0.221	19.87	0.695	0.274	18.69	0.643	0.330
Pose- free	NoPoSplat [ 59 ] (ICLR25)	16.13	0.447	0.497	14.73	0.392	0.603	13.82	0.365	0.640
	AnySplat [ 16 ] (SIGGRAPHAsia25)	18.72	0.551	0.310	18.40	0.533	0.333	18.36	0.529	0.337
	WorldMirror [ 26 ] (arXiv25)	20.44	0.625	0.278	19.78	0.594	0.312	19.67	0.589	0.316

Table 2: Quantitative comparison of NVS performance on the DL3DV dataset [ 23 ] under various input-view settings.

Figure 6: Qualitative comparison of NVS performance on DL3DV dataset [ 23 ] .

Methods		16 Views			20 Views			24 Views
Methods		PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
w/ Pose	MonoSplat [ 25 ] (CVPR25)	17.81	0.642	0.345	17.48	0.625	0.361	17.23	0.611	0.372
	MVSplat [ 8 ] (ECCV24)	18.19	0.502	0.376	18.12	0.500	0.379	18.09	0.500	0.379
	DepthSplat [ 56 ] (CVPR25)	20.41	0.713	0.268	19.92	0.694	0.286	19.78	0.688	0.289
Pose- free	NoPoSplat [ 59 ] (ICLR25)	22.30	0.668	0.286	22.24	0.666	0.288	22.25	0.666	0.288
	AnySplat [ 16 ] (SIGGRAPHAsia25)	21.89	0.615	0.275	22.05	0.622	0.256	21.96	0.619	0.258

Table 3: Cross-dataset generalization performance under various input-view settings. We evaluate the zero-shot performance on the ACID dataset [ 24 ] .

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 9: Pose-Geometry Discrepancy. To validate that this structural misalignment is not confined to a specific baseline, we visualize the consistent spatial drift across distinct models and diverse scenes: (a) DepthAnything3 [ 22 ] evaluated on the DL3DV dataset [ 23 ] , and (b) SPFSplat [ 14 ] on the RE10K dataset [ 66 ] .

Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
SPFSplat [ 14 ]	21.32	0.694	0.266
SPFSplat +AirSplat [ 14 ]	22.76	0.793	0.206
WorldMirror [ 26 ]	21.08	0.701	0.274
WorldMirror [ 26 ] +AirSplat	23.66	0.811	0.182
DA3 [ 22 ]	21.06	0.710	0.254
DA3 +AirSplat [ 22 ]	23.77	0.814	0.178

Appendix

Table 5: Additional baselines results on RE10K dataset under 24 input views.

Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
Baseline	20.74	0.691	0.242
Baseline + Context-only Training	21.39	0.717	0.233
Baseline + Context-Target Training	21.52	0.727	0.221
Baseline + Context-Target w/ SCPA Training	22.29	0.740	0.214
Baseline + ROM	22.11	0.731	0.218
Ours (Full)	22.50	0.747	0.207

Appendix

Table 6: Ablation analysis on DL3DV dataset [ 23 ] under 12 input views.

Method	View	Acc. $\downarrow$		Comp. $\downarrow$		NC. $\uparrow$
Method	View	Mean	Med.	Mean	Med.	Mean	Med.
VGGT [ 49 ]	sparse	0.044	0.025	0.056	0.033	0.733	0.845
AnySplat [ 16 ]		0.080	0.053	0.120	0.072	0.684	0.785
$\pi^{3}$ [ 53 ]		0.047	0.029	0.075	0.049	0.742	0.841
DA3 [ 22 ] /AirSplat		0.049	0.034	0.065	0.046	0.757	0.866
VGGT [ 49 ]	dense	0.022	0.008	0.026	0.012	0.666	0.760

Appendix

Table 7: Pose-free point map estimation on 7-Scenes [ 42 ] dataset.

Methods	Avg. Time (s) / Iter.
Baseline + Context-Target	2.35
Baseline + Context-Target w/ SCPA	3.67
Ours (Full Model)	3.89

Appendix

Table 8: Training time analysis. We report the average time per training iteration. While our proposed modules (SCPA and ROM) introduce additional forward passes and teacher model evaluations, the overall training remains tractable.

Figure 10: Qualitative comparison of NVS performance on DL3DV dataset [ 23 ] .

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SPFSplatV2: Efficient Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views

Ranran Huang, Krystian Mikolajczyk

Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, United Kingdom

3D Gaussian Splatting 3D Reconstruction Camera Pose Estimation Novel View Synthesis Sparse-View 3D Reconstruction Feed-Forward 3D Reconstruction

Open paper AskPDF arXiv

We introduce SPFSplatV2, an efficient feed-forward framework for 3D Gaussian splatting from sparse multi-view images, requiring no ground-truth poses during training or inference. The framework employs a shared feature extraction backbone to jointly predict 3D Gaussian primitives and camera poses in a canonical space from unposed inputs. To enable efficient and accurate pose estimation, we introduce a masked attention mechanism for target-view pose prediction and a reprojection loss that enforces pixel-aligned Gaussian primitives, providing stronger geometric constraints. We further demonstrate the compatibility of our training framework with different reconstruction architectures, resulting in two model variants. Remarkably, despite the absence of pose supervision, our method achieves state-of-the-art performance in both in-domain and out-of-domain novel view synthesis, even under extreme viewpoint changes and limited image overlap. It also surpasses many methods that rely on geometric supervision in relative pose estimation. By eliminating dependence on ground-truth poses, our method offers the scalability to leverage larger and more diverse datasets. Code and pretrained models will be available on our project page: https://ranrhuang.github.io/spfsplatv2/.

Abstract

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