重新审视光度歧义以实现高精度高斯-散射表面重建
Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction
AI总结：
本文提出AmbiSuR框架，通过高斯散射内在解决方案解决光度歧义问题，提升3D表面重建性能，实验显示在多种挑战场景中表现优异。
摘要：
可微渲染的表面重建近年来取得了显著成效，但普遍存在的光度歧义严重限制了现有方法。本文提出AmbiSuR框架，探索高斯散射内在解决方案，实现高精度光度歧义鲁棒的3D表面重建。通过重新审视基础，发现表示中存在两种内置的原始元歧义，并揭示高斯散射中固有的歧义自指示潜力。基于此，首次引入光度消歧，约束不成立的几何解以形成确定性表面。随后，提出歧义指示模块，释放自指示潜力以识别并进一步指导修正欠约束重建。大量实验显示，我们的表面重建在各种挑战场景中优于现有方法，表现出广泛的兼容性。项目：https://fictionarry.github.io/AmbiSuR-Proj/
英文摘要：
Surface reconstruction with differentiable rendering has achieved impressive performance in recent years, yet the pervasive photometric ambiguities have strictly bottlenecked existing approaches. This paper presents AmbiSuR, a framework that explores an intrinsic solution upon Gaussian Splatting for the photometric ambiguity-robust surface 3D reconstruction with high performance. Starting by revisiting the foundation, our investigation uncovers two built-in primitive-wise ambiguities in representation, while revealing an intrinsic potential for ambiguity self-indication in Gaussian Splatting. Stemming from these, a photometric disambiguation is first introduced, constraining ill-posed geometry solution for definite surface formation. Then, we propose an ambiguity indication module that unleashes the self-indication potential to identify and further guide correcting underconstrained reconstructions. Extensive experiments demonstrate our superior surface reconstructions compared to existing methods across various challenging scenarios, excelling in broad compatibility. Project: https://fictionarry.github.io/AmbiSuR-Proj/ .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
