Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction

• URL: http://arxiv.org/abs/2605.12494v1
• Date: Tue, 12 May 2026 17:59:47 GMT
• Title: Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction
• Abstract summary: AmbiSuR is a framework that explores an intrinsic solution upon Gaussian Splatting for the photometric ambiguity-robust surface 3D reconstruction with high performance.<n>Our investigation uncovers two built-in primitive-wise ambiguities in representation, while revealing an intrinsic potential for ambiguity self-indication in Gaussian Splatting.<n>We propose an ambiguity indication module that unleashes the self-indication potential to identify and further guide correcting underconstrained reconstructions.
• Score: 62.73945158853891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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/ .

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