Complete Shape

Complete shape reconstruction aims to recover full 3D object shapes from partial observations, a crucial task in computer vision and related fields. Current research heavily emphasizes self-supervised learning methods, employing architectures like diffusion models and neural implicit representations, to overcome limitations of supervised approaches that require large datasets of complete shapes. These advancements are improving the accuracy and diversity of shape completion, particularly for real-world scenarios with noisy or incomplete data, impacting applications such as robotic grasping, 3D scene reconstruction, and object-level mapping. The development of efficient and robust algorithms for handling diverse object shapes and poses in complex scenes remains a key focus.