Point-Based 3D Reconstruction from Sparse Views under Known Illumination

2026-08-20

AI Summary

该研究提出了一种基于不透明度的beta surfels的可微点渲染方法，通过物理基础的光线传输约束重建，在稀疏视图下实现精确的3D重建。

Abstract

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.

Problem

• Sparse view 3D reconstruction
• known illumination
• point-based representation
• light transport
• beta surfels

Innovation

• differentiable point rendering
• opacity-bearing beta surfels
• adjoint light transport formulation
• physically based light transport
• surface reconstruction