解决高斯点云重建中的光照模糊问题，提升表面精度与兼容性。
Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction
- 发现高斯点云的两种固有表示模糊性，提出光照去模糊机制。
- 引入自指示模块，自动识别并修正欠约束重建结果。
- 在多种复杂场景下优于现有方法，适合高精度三维重建任务。
基于可微渲染的表面重建近年来取得显著进展，但普遍存在的光照模糊问题严重制约了现有方法。本文提出AmbiSuR框架，针对高斯点云提出一种内在的光照模糊鲁棒性表面三维重建方案。通过重新审视基础，研究揭示了表示层面存在的两种固有逐原语模糊性，并发现高斯点云中蕴含的模糊自指示潜力。基于此，首次引入光照去模糊机制，约束病态几何解以形成确定表面；进一步提出模糊指示模块，释放自指示能力以识别并引导修正欠约束重建。大量实验表明，本方法在多种挑战性场景下均优于现有技术，展现出优异的广泛兼容性。
原文摘要 · Abstract (English)
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/ .
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