Shape Completion

Shape completion aims to reconstruct a complete 3D object from partial observations, addressing challenges like occlusions and incomplete scans. Current research focuses on developing self-supervised and weakly-supervised learning methods, employing architectures like diffusion models, transformers, and autoencoders to generate realistic and diverse completions. These advancements are significantly impacting fields like robotics (improving grasping and manipulation), medical imaging (enhancing organ segmentation and surgical planning), and agriculture (optimizing yield estimation and automated harvesting), by providing more complete and accurate 3D representations of objects and scenes.