3 accepted papers
Robust robotic grasping in cluttered environments presents a significant challenge, as existing methods often neglect the complex interactions between the gripper, objects, and obstacles, leading to collisions and grasping failures. To address this, we propose a framework that integrates collision a
Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i.e., filling the regions that were missing in the shape. These methods, however, only complete the partial shape with a single output, ignoring the ambiguity when reasoning the missing geomet…
We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly. The input to our network is a 3D shape segmented into parts, where each part is first encoded into a feature representation using a part autoencoder. The core component of PQ-NET is a se…