MuGS: Multi-Baseline Generalizable Gaussian Splatting Reconstruction

• URL: http://arxiv.org/abs/2508.04297v1
• Date: Wed, 06 Aug 2025 10:34:24 GMT
• Title: MuGS: Multi-Baseline Generalizable Gaussian Splatting Reconstruction
• Authors: Yaopeng Lou, Liao Shen, Tianqi Liu, Jiaqi Li, Zihao Huang, Huiqiang Sun, Zhiguo Cao,
• Abstract summary: We present Multi-Baseline Gaussian Splatting (MuRF), a feed-forward approach for novel view synthesis.<n>MuRF achieves state-of-the-art performance across multiple baseline settings and diverse scenarios.
• Score: 13.941042770932794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Multi-Baseline Gaussian Splatting (MuRF), a generalized feed-forward approach for novel view synthesis that effectively handles diverse baseline settings, including sparse input views with both small and large baselines. Specifically, we integrate features from Multi-View Stereo (MVS) and Monocular Depth Estimation (MDE) to enhance feature representations for generalizable reconstruction. Next, We propose a projection-and-sampling mechanism for deep depth fusion, which constructs a fine probability volume to guide the regression of the feature map. Furthermore, We introduce a reference-view loss to improve geometry and optimization efficiency. We leverage 3D Gaussian representations to accelerate training and inference time while enhancing rendering quality. MuRF achieves state-of-the-art performance across multiple baseline settings and diverse scenarios ranging from simple objects (DTU) to complex indoor and outdoor scenes (RealEstate10K). We also demonstrate promising zero-shot performance on the LLFF and Mip-NeRF 360 datasets.

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