提出物理驱动的光度捆绑调整，提升非朗伯环境下的三维重建精度。
Physically-Based Photometric Bundle Adjustment in Non-Lambertian Environments
- 引入材质、光照与光路的物理权重，区分像素对的光度不一致性
- 在非朗伯场景中实现更高精度的相机位姿与三维几何估计
- 构建首个含完整光照和材质真值的SLAM相关非朗伯数据集
光度捆绑调整（PBA）广泛用于通过假设朗伯世界来估计相机位姿和三维结构。然而，真实环境中普遍存在非漫反射现象，导致光度一致性假设被破坏，严重影响现有PBA方法的可靠性。为此，本文提出一种新型物理驱动的PBA方法：引入材质、光照与光路相关的物理权重，以区分不同水平的光度不一致性；基于序列图像设计材质估计模型，基于点云设计光照估计模型；并建立首个包含完整光照与材质真值的非朗伯场景SLAM相关数据集。大量实验表明，该方法在准确性上优于现有方法。
原文摘要 · Abstract (English)
Photometric bundle adjustment (PBA) is widely used in estimating the camera pose and 3D geometry by assuming a Lambertian world. However, the assumption of photometric consistency is often violated since the non-diffuse reflection is common in real-world environments. The photometric inconsistency significantly affects the reliability of existing PBA methods. To solve this problem, we propose a novel physically-based PBA method. Specifically, we introduce the physically-based weights regarding material, illumination, and light path. These weights distinguish the pixel pairs with different levels of photometric inconsistency. We also design corresponding models for material estimation based on sequential images and illumination estimation based on point clouds. In addition, we establish the first SLAM-related dataset of non-Lambertian scenes with complete ground truth of illumination and material. Extensive experiments demonstrated that our PBA method outperforms existing approaches in accuracy.
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