DeltaSplat: Iterative Gaussian Refinement for Pose-Free Feed-Forward 3D Gaussian Splatting

Chanung Park; Seunghyeon Song; Joo Chan Lee; Eunbyung Park; Jong Hwan Ko

cs.CVOct 7, 2026

DeltaSplat: Iterative Gaussian Refinement for Pose-Free Feed-Forward 3D Gaussian Splatting

Authors: Chanung Park, Seunghyeon Song, Joo Chan Lee, Eunbyung Park, Jong Hwan Ko

Organizations: Department of Electrical and Computer Engineering, Sungkyunkwan University · Department of Artificial Intelligence, Sungkyunkwan University · Department of Artificial Intelligence, Yonsei University

Abstract

Pose-free feed-forward 3D Gaussian Splatting (3DGS) reconstructs a scene from sparse, unposed images in a single network pass, removing the need for camera calibration and per-scene optimization. However, camera estimation errors propagate into the predicted Gaussians and compound the geometric and photometric inaccuracies of single-pass prediction. To correct these errors, we introduce DeltaSplat, a lightweight Gaussian refinement module for pose-free feed-forward 3DGS. It iteratively renders the current Gaussians at the input context views and predicts per-Gaussian updates from the resulting residuals. A 2D residual alone, however, underdetermines the 3D correction. DeltaSplat therefore conditions each update on per-pixel Plücker rays and rendered depth as a soft geometric prior. A dual-branch convolutional mixer efficiently encodes these inputs, and per-attribute heads decode the fused features into position, opacity, and color updates. The module adds only ~2.2% parameters to the backbone and remains fully feed-forward at inference. On DL3DV, DeltaSplat reaches 26.64 dB PSNR in the pose-free setting, improving its state-of-the-art backbone by 1.75 dB and surpassing even baselines supplied with ground-truth cameras; consistent gains hold across 6-24 views and all camera regimes.

Figures & tables

Figure 2: Method overview. The backbone predicts cameras and initial Gaussians; at each iteration, the refinement module encodes the render residual with the Plücker rays and the rendered depth, and decodes the per-Gaussian update $\{\Delta\mu,\Delta c,\Delta\alpha\}$ , repeated $K{=}3$ times with shared weights.

Figure 3: Geometry-guided residual encoding. (a) The 2D residual alone underdetermines the position update $\Delta\mu$ : displacements along the viewing ray yield identical residuals, leaving a large possible region (red). (b) The per-pixel Plücker ray and rendered depth encode the line of sight and the current prediction’s position along it, a prior on the current geometry under which the possible region contracts.

			6 views			12 views			24 views
Method	$p$	$K$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
MVSplat ( Chen et al., 2024 )	✓	✓	22.66	0.760	0.173	21.29	0.709	0.224	19.98	0.662	0.269
DepthSplat ( Xu et al., 2025b )	✓	✓	23.42	0.797	0.136	21.91	0.753	0.179	20.09	0.690	0.240
YoNoSplat ( Ye et al., 2026 )	✓	✓	24.72	0.817	0.139	23.29	0.773	0.177	22.67	0.758	0.192
DeltaSplat	✓	✓	26.70	0.866	0.113	25.33	0.833	0.142	24.72	0.824	0.151
NoPoSplat ( Ye et al., 2025 )		✓	22.77	0.743	0.179	19.38	0.563	0.318	17.86	0.495	0.397

Table 1: Main results on the DL3DV testset (140 scenes,

224^{2}

) at 6 / 12 / 24 context views.

p

K

: ✓ means the ground-truth pose / intrinsics are supplied, blank means predicted by the model.

Panel of Figure 4 — Figure 4: Qualitative results on DL3DV scenes.

	32 views			64 views			128 views
Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
YoNoSplat	17.94	0.659	0.380	18.83	0.688	0.342	19.28	0.701	0.325
DeltaSplat	18.62	0.670	0.371	19.76	0.711	0.328	20.62	0.741	0.298

Table 2: Cross-dataset generalization to ScanNet++ ( Yeshwanth et al., 2023 ) at

224^{2}

with 32 / 64 / 128 context views and known intrinsics. All models are trained on DL3DV.

Figure 6: Effect of the 3D geometry prior. Each pair compares the geometry-aimed encoding with an encoding built from the 2D residual alone; the left pair shows the refined render and the right pair the per-Gaussian $|\Delta\mu|$ . Without the Plücker-ray and depth channels the same residual drives larger and more scattered position updates, and PSNR falls from $25.1$ to $20.3$ dB.

Table 4: Ablations on the DL3DV test set in the pose-free setting with known intrinsics (6 context views). (a) Each row removes a single component from the full model. (b) Effect of the number of refinement iterations

K

applied at test time, with per-scene refinement time;

K{=}0

denotes the unrefined backbone output, and the model is trained with

K{=}3

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ZipSplat: Fewer Gaussians, Better Splats

Alexander Veicht, Sunghwan Hong, Dániel Baráth, Marc Pollefeys

ETH Zürich · Microsoft

3D Gaussian Splatting Multi-View 3D Reconstruction Feed-Forward 3D Reconstruction

Open paper AskPDF arXiv

Feed-forward 3D Gaussian Splatting methods reconstruct a scene from posed or pose-free images in a single forward pass, yet current approaches predict one Gaussian per input pixel, tying the representation budget to camera resolution rather than scene complexity. A flat wall and a richly textured object thus produce equally many Gaussians despite very different geometric needs. We propose ZipSplat, a token-based feed-forward model that decouples Gaussian placement from the pixel grid. A multi-view backbone extracts dense visual tokens, and k-means clustering compresses them into a compact set of scene tokens. Cross- and self-attention refine these tokens, and a lightweight MLP decodes each into a group of Gaussians with unconstrained 3D positions. Because clustering is applied at inference, a single trained model spans the quality-efficiency curve without retraining. ZipSplat operates without ground-truth poses or intrinsics, yet sets a new state of the art on DL3DV and RealEstate10K with

{\sim}6{\times}

fewer Gaussians than pixel-aligned methods, surpassing the best pose-free baseline by 2.1dB and 1.2dB PSNR, respectively. It further generalizes zero-shot to Mip-NeRF360 and ScanNet++, outperforming all comparable baselines. Our project page is at https://veichta.com/zipsplat.

Abstract

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