DGS-Net: Learning Dexterous Grasps from Single-View Point Clouds via a Multi-Object Scene Dataset
DGS-Net is an end-to-end network designed to predict dense grasp configurations from single-view point clouds in complex multi-object scenes. It tackles dexterous grasping by explicitly modeling grasp offsets and pre-grasp gestures, leading to robust predictions. The resulting dataset and model show high success rates in simulation and real robotics, demonstrating strong generalization.