Object Completion

Object completion aims to reconstruct missing parts of an object from partial observations, a crucial task in computer vision and 3D perception. Current research focuses on developing robust algorithms, often employing transformer networks, diffusion models, or encoder-decoder architectures, to generate realistic and accurate completions from single or multiple reference images, point clouds, or RGB-D scans. These advancements improve the accuracy and efficiency of 3D object detection, scene understanding, and other applications requiring complete object representations. The resulting improvements in object representation have significant implications for various fields, including robotics, autonomous driving, and augmented reality.