Hand Held Object Reconstruction

Hand-held object reconstruction aims to create 3D models of objects grasped by a hand, a challenging task due to occlusion and limited visible object surface. Current research focuses on leveraging estimated 3D hand poses and employing various deep learning architectures, including diffusion models, occupancy networks, and graph neural networks, often incorporating multi-view or synthetic data to improve reconstruction accuracy, especially in the hand-object contact region. These advancements are significant for robotics, augmented reality, and computer vision, enabling more robust object manipulation and scene understanding in complex, real-world scenarios. The development of novel sensor modalities, such as flexible tactile sensors, further enhances the accuracy and reliability of these reconstruction methods.