Reflective Surface

Reflective surface analysis focuses on accurately reconstructing and rendering 3D models of reflective objects, a challenging task due to view-dependent specular reflections. Current research employs neural networks, including implicit neural representations and convolutional recurrent networks, to address this challenge, often incorporating techniques like polarized imaging and Gaussian splatting for improved accuracy and real-time performance. These advancements have implications for various fields, including computer vision (3D reconstruction), virtual reality (eye tracking), and robotics (surface manipulation and cleaning), enabling more realistic simulations and improved automation.