Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction

Problem
This paper addresses the limitations of existing surface reconstruction methods that utilize differentiable rendering, specifically focusing on the pervasive issue of photometric ambiguities that hinder accurate 3D reconstruction. The authors identify a gap in the literature regarding the intrinsic ambiguities present in Gaussian Splatting representations. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce AmbiSuR, a novel framework that leverages Gaussian Splatting to achieve robust surface reconstruction in the presence of photometric ambiguities. The core technical contributions include:

1. Photometric Disambiguation: This module constrains the geometry solution to mitigate ill-posed scenarios, thereby facilitating the formation of definite surfaces.
2. Ambiguity Indication Module: This component exploits the self-indication potential inherent in Gaussian Splatting to identify and correct underconstrained reconstructions. The architecture integrates these modules into the Gaussian Splatting pipeline, enhancing its capability to handle ambiguities effectively. The authors do not disclose specific training compute or dataset details, but they emphasize extensive experimentation across various challenging scenarios.

Results
The experimental results demonstrate that AmbiSuR significantly outperforms existing surface reconstruction methods. The authors report improvements in reconstruction accuracy across multiple benchmarks, although specific numerical results and effect sizes are not detailed in the abstract. The framework exhibits broad compatibility, suggesting its applicability to diverse reconstruction tasks. Comparisons are made against named baselines, but exact performance metrics are not provided in the summary.

Limitations
The authors acknowledge that while AmbiSuR addresses photometric ambiguities effectively, it may still struggle with extreme lighting conditions or highly reflective surfaces, which could introduce additional complexities. They do not discuss potential computational overhead introduced by the added modules, nor do they address the scalability of the approach to real-time applications.

Why it matters
This work has significant implications for the field of 3D reconstruction, particularly in applications requiring high fidelity and robustness against photometric variations, such as augmented reality, robotics, and computer graphics. By providing a framework that effectively manages photometric ambiguities, AmbiSuR could pave the way for more accurate and reliable surface reconstruction techniques, influencing future research directions and practical implementations in the domain.

Authors: Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng, Xiaohan Yu, Lin Gu, Gim Hee Lee
Source: arXiv:2605.12494
URL: https://arxiv.org/abs/2605.12494v1