ProSplat: Improved Feed-Forward 3D Gaussian Splatting for Wide-Baseline Sparse Views

• URL: http://arxiv.org/abs/2506.07670v1
• Date: Mon, 09 Jun 2025 11:45:50 GMT
• Title: ProSplat: Improved Feed-Forward 3D Gaussian Splatting for Wide-Baseline Sparse Views
• Authors: Xiaohan Lu, Jiaye Fu, Jiaqi Zhang, Zetian Song, Chuanmin Jia, Siwei Ma,
• Abstract summary: ProSplat is a two-stage feed-forward framework for high-fidelity rendering under wide-baseline conditions.<n>ProSplat achieves an average improvement of 1 dB in PSNR compared to recent SOTA methods.
• Score: 31.282881864317375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feed-forward 3D Gaussian Splatting (3DGS) has recently demonstrated promising results for novel view synthesis (NVS) from sparse input views, particularly under narrow-baseline conditions. However, its performance significantly degrades in wide-baseline scenarios due to limited texture details and geometric inconsistencies across views. To address these challenges, in this paper, we propose ProSplat, a two-stage feed-forward framework designed for high-fidelity rendering under wide-baseline conditions. The first stage involves generating 3D Gaussian primitives via a 3DGS generator. In the second stage, rendered views from these primitives are enhanced through an improvement model. Specifically, this improvement model is based on a one-step diffusion model, further optimized by our proposed Maximum Overlap Reference view Injection (MORI) and Distance-Weighted Epipolar Attention (DWEA). MORI supplements missing texture and color by strategically selecting a reference view with maximum viewpoint overlap, while DWEA enforces geometric consistency using epipolar constraints. Additionally, we introduce a divide-and-conquer training strategy that aligns data distributions between the two stages through joint optimization. We evaluate ProSplat on the RealEstate10K and DL3DV-10K datasets under wide-baseline settings. Experimental results demonstrate that ProSplat achieves an average improvement of 1 dB in PSNR compared to recent SOTA methods.

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