Variation-Robust Few-Shot 3D Affordance Segmentation for Robotic Manipulation

Dingchang Hu, Tianyu Sun, Pengwei Xie, Siang Chen, Huazhong Yang, Guijin Wang

Abstract: Traditional affordance segmentation on 3D point cloud objects requires massive amounts of annotated training data and can only make predictions within predefined classes and affordance tasks. To overcome these limitations, we propose a variation-robust few-shot 3D affordance segmentation network (VRNet) for robotic manipulation, which requires only several affordance annotations for novel object classes and manipulation tasks. In particular, we design an orientation-tolerant feature extractor to address pose variation between support and query point cloud objects, and present a multi-scale label propagation algorithm for variation in completeness. Extensive experiments on affordance datasets show that VRNet provides the best segmentation performance compared with previous works. Moreover, experiments in real robotic scenarios demonstrate the generalization ability of our method.