ArXiv

Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction

2026-05-12 · 17:59 UTC ·Jiahe Li, Jiawei Zhang, Xiao Bai... ·1 min read
Authors
Jiahe Li, Jiawei Zhang, Xiao Bai...
Categories
cs.CV
arXiv
https://arxiv.org/abs/2605.12494v1
PDF
https://arxiv.org/pdf/2605.12494v1

Brief

AmbiSuR revisits Gaussian Splatting to improve photometric‑ambiguity‑robust 3D surface reconstruction. The authors uncover two primitive‑wise ambiguities and an intrinsic self‑indication ability in the representation, then introduce photometric disambiguation and an ambiguity‑indication module to constrain and correct geometry. Experiments reportedly yield superior reconstructions across challenging scenes; paper on arXiv (2026-05-12) and accepted at ICML 2026.

Source evidence

Abstract

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/ .

Comment: Accepted at ICML 2026. Project page: https://fictionarry.github.io/AmbiSuR-Proj/