Non Lambertian Surface

Non-Lambertian surfaces, which deviate from the ideal diffuse reflection assumed by Lambertian models, pose significant challenges in computer vision tasks like depth estimation and 3D reconstruction. Current research focuses on developing robust algorithms, often employing neural radiance fields (NeRFs) or multi-view stereo (MVS) techniques, to accurately capture the complex view-dependent reflectance properties of these surfaces. These advancements are crucial for improving the realism and accuracy of augmented reality applications, robotic perception, and medical imaging, where accurate 3D modeling of non-Lambertian objects (e.g., shiny or translucent materials) is essential. The development of novel loss functions and data augmentation strategies are also key areas of focus to improve the robustness and accuracy of these methods.