已知光照条件下基于点的稀疏视图三维重建
Point-Based 3D Reconstruction from Sparse Views under Known Illumination
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- Aalborg University（奥尔堡大学）
- Centre for Software Technology, University of Southern Denmark（南丹麦大学软件技术中心）
- Pioneer Centre for Artificial Intelligence（先锋人工智能中心）
- Technical University of Denmark（丹麦技术大学）
- Technische Universität Berlin（柏林工业大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
提出带不透明度的β表面元可微点渲染方法，在5个合成物体的10个位姿视图重建中，平均 chamfer 距离比最强基线低28.5%，仅需267个表面元，实现高效高精度三维重建。
中文摘要 AI 辅助
稀疏视图三维重建通常采用神经隐式曲面或高斯溅射（Gaussian splatting）等密集基于点的表示方法。感知表面的溅射方法通过定向基元与正则化提升提取的几何结构，而RadiosityGS则结合了基于辐射度启发的有限元表面元（surfel）公式的可微光传输。我们提出一种基于带不透明度的β表面元（beta surfels）的可微点渲染方法。不透明度显式伴随光传输公式为表面元的几何与外观参数提供梯度，使基于物理的光传输能够约束重建过程。在从10个位姿视图重建的5个合成物体上，我们的方法在评估的基线中实现了最低的平均对称 chamfer距离，且仅使用平均267个表面元（比最强的基于点的基线少约161个基元），将平均 chamfer距离降低了28.5%。定向 chamfer结果进一步表明，与相关基于点的方法相比，该方法在精度和完整性上均有提升。这些结果显示，在受控直接光照场景下，紧凑的β表面元结合基于传输的优化可恢复曲面，无需依赖评估基线所用的数万至数十万个基元。
英文摘要
Sparse view 3D reconstruction is commonly addressed with neural implicit surfaces or dense point-based representations such as Gaussian splatting. Surface-aware splatting methods improve extracted geometry through oriented primitives and regularization, while RadiosityGS incorporates differentiable light transport through a radiosity inspired finite-element surfel formulation. We propose a differentiable point rendering method based on opacity-bearing beta surfels. An opacity explicit adjoint light transport formulation provides gradients for surfel geometry and appearance parameters, allowing physically based light transport to constrain reconstruction. Across five synthetic objects reconstructed from ten posed views, our method achieves the lowest mean symmetric Chamfer distance among the evaluated baselines and reduces mean Chamfer distance by 28.5% relative to the strongest point-based baseline while using only 267 surfels on average, approximately ~161 fewer primitives. Directional Chamfer results further show improved accuracy and competitive completion relative to related point-based methods. These results show that, in the controlled direct illumination setting, compact beta surfels combined with transport-based optimization can recover surfaces without relying on the tens to hundreds of thousands of primitives used by the evaluated baselines.
