+ Accurate geometric surface reconstruction, providing essential environmental information for navigation and manipulation tasks, is critical for enabling robotic + self-exploration and interaction. Recently, 3D Gaussian Splatting (3DGS) has gained significant attention in the field of surface reconstruction due to its impressive + geometric quality and computational efficiency. While recent relevant advancements in novel view synthesis under inconsistent illumination using 3DGS have shown promise, + the challenge of robust surface reconstruction under such conditions is still being explored. To address this challenge, we propose a method called GS-3I. + Specifically, to mitigate 3D Gaussian optimization bias caused by underexposed regions in single-view images, based on Convolutional Neural Network (CNN), a tone mapping + correction framework is introduced. Furthermore, inconsistent lighting across multi-view images, resulting from variations in camera settings and complex scene illumination, + often leads to geometric constraint mismatches and deviations in the reconstructed surface. To overcome this, we propose a normal compensation mechanism that integrates + reference normals extracted from single-view image with normals computed from multi-view observations to effectively constrain geometric inconsistencies. Extensive + experimental evaluations demonstrate that GS-3I can achieve robust and accurate surface reconstruction across complex illumination scenarios, highlighting + its effectiveness and versatility in this critical challenge.https://github.com/TFwang-9527/GS-3I +
+
+ @article{2025GS-3I,
+ title={GS-3I: Gaussian Splatting for Surface Reconstruction from Illumination-Inconsistent Images},
+ author={Tengfei Wang, Yongmao Hou, Zhaoning Zhang, Yiwei Xu, Zongqian Zhan and Xin Wang},
+ booktitle={arxiv preprint arxiv:},
+ year={2025}
+ }
+ ```