Advancing Structured Priors for Sparse-Voxel Surface Reconstruction
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911396999987200 |
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| author | Chi, Ting-Hsun Chen, Chu-Rong Hsu, Chi-Tun Lin, Hsuan-Ting Huang, Sheng-Yu Sun, Cheng Wang, Yu-Chiang Frank |
| author_facet | Chi, Ting-Hsun Chen, Chu-Rong Hsu, Chi-Tun Lin, Hsuan-Ting Huang, Sheng-Yu Sun, Cheng Wang, Yu-Chiang Frank |
| contents | Reconstructing accurate surfaces with radiance fields has progressed rapidly, yet two promising explicit representations, 3D Gaussian Splatting and sparse-voxel rasterization, exhibit complementary strengths and weaknesses. 3D Gaussian Splatting converges quickly and carries useful geometric priors, but surface fidelity is limited by its point-like parameterization. Sparse-voxel rasterization provides continuous opacity fields and crisp geometry, but its typical uniform dense-grid initialization slows convergence and underutilizes scene structure. We combine the advantages of both by introducing a voxel initialization method that places voxels at plausible locations and with appropriate levels of detail, yielding a strong starting point for per-scene optimization. To further enhance depth consistency without blurring edges, we propose refined depth geometry supervision that converts multi-view cues into direct per-ray depth regularization. Experiments on standard benchmarks demonstrate improvements over prior methods in geometric accuracy, better fine-structure recovery, and more complete surfaces, while maintaining fast convergence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17720 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Advancing Structured Priors for Sparse-Voxel Surface Reconstruction Chi, Ting-Hsun Chen, Chu-Rong Hsu, Chi-Tun Lin, Hsuan-Ting Huang, Sheng-Yu Sun, Cheng Wang, Yu-Chiang Frank Computer Vision and Pattern Recognition Reconstructing accurate surfaces with radiance fields has progressed rapidly, yet two promising explicit representations, 3D Gaussian Splatting and sparse-voxel rasterization, exhibit complementary strengths and weaknesses. 3D Gaussian Splatting converges quickly and carries useful geometric priors, but surface fidelity is limited by its point-like parameterization. Sparse-voxel rasterization provides continuous opacity fields and crisp geometry, but its typical uniform dense-grid initialization slows convergence and underutilizes scene structure. We combine the advantages of both by introducing a voxel initialization method that places voxels at plausible locations and with appropriate levels of detail, yielding a strong starting point for per-scene optimization. To further enhance depth consistency without blurring edges, we propose refined depth geometry supervision that converts multi-view cues into direct per-ray depth regularization. Experiments on standard benchmarks demonstrate improvements over prior methods in geometric accuracy, better fine-structure recovery, and more complete surfaces, while maintaining fast convergence. |
| title | Advancing Structured Priors for Sparse-Voxel Surface Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.17720 |