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About this paper
Ting-Hsun Chi, Chu-Rong Chen, Chi-Tun Hsu, Hsuan-Ting Lin, Sheng-Yu Huang, Cheng Sun, Yu-Chiang Frank Wang
This research introduces a framework that enhances sparse-voxel surface reconstruction by combining a structured, geometry-aware initialization with refined depth supervision. The approach achieves a mean Chamfer Distance of 0.46 on the DTU dataset, outperforming existing methods, and demonstrates faster, more stable training convergence.
Key insights
- A structured and geometry-informed initialization (VILOD) is crucial for efficient and accurate sparse-voxel surface reconstruction, leading to faster and more stable training.
- Direct and periodically updated per-pixel depth supervision (RDGS) provides a powerful signal to refine geometry effectively, especially when coupled with a strong initial geometric estimate.
- The synergistic combination of both a well-initialized sparse voxel representation and an effective depth refinement mechanism is essential for achieving superior geometric accuracy and preserving fine details.
Summary by alphaXiv.
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