3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding

1 code implementation • CVPR 2021 • Shengheng Deng, Xun Xu, Chaozheng Wu, Ke Chen, Kui Jia

The ability to understand the ways to interact with objects from visual cues, a. k. a.

Affordance Detection Benchmarking +1

Learning Category-level Shape Saliency via Deep Implicit Surface Networks

no code implementations • 14 Dec 2020 • Chaozheng Wu, Lin Sun, Xun Xu, Kui Jia

Given the large shape variations among different instances of a same category, we are formally interested in developing a quantity defined for individual points on a continuous object surface; the quantity specifies how individual surface points contribute to the formation of the shape as the category.

Point Cloud Classification Saliency Prediction

Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps

2 code implementations • NeurIPS 2020 • Chaozheng Wu, Jian Chen, Qiaoyu Cao, Jianchi Zhang, Yunxin Tai, Lin Sun, Kui Jia

To test GPNet, we contribute a synthetic dataset of 6-DOF object grasps; evaluation is conducted using rule-based criteria, simulation test, and real test.

Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors

no code implementations • 14 Jan 2020 • Lulu Tang, Ke Chen, Chaozheng Wu, Yu Hong, Kui Jia, Zhi-Xin Yang

Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from global configuration of local geometries in a supervised learning manner.