Non Lambertian

Non-Lambertian surfaces, which deviate from the ideal diffuse reflection assumed in many computer vision models, pose significant challenges for accurate 3D shape and reflectance recovery. Current research focuses on developing robust methods for photometric stereo and multi-view stereo that account for complex, non-diffuse reflections, often employing deep learning architectures and physically-based models like Blinn-Phong to handle specularities and shadows. These advancements are crucial for improving the accuracy of 3D scene reconstruction in real-world applications such as augmented reality, robotics, and industrial inspection, where non-Lambertian materials are prevalent. The development of large-scale datasets and improved algorithms is driving progress in this field.