187 citations · 208 across the 6 of their papers we have counts for
6 papers
Multiview Compressive Coding for 3D Reconstruction
Chao-Yuan Wu, Justin Johnson, Jitendra Malik +2
A central goal of visual recognition is to understand objects and scenes from a single image. 2D recognition has witnessed tremendous progress thanks to large-scale learning and ge…
A ConvNet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu +3
The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification mo…
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding
Shengheng Deng, Xun Xu, Chaozheng Wu +2
The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing,…
Learning Category-level Shape Saliency via Deep Implicit Surface Networks
Chaozheng Wu, Lin Sun, Xun Xu +1
This paper is motivated from a fundamental curiosity on what defines a category of object shapes. For example, we may have the common knowledge that a plane has wings, and a chair…
Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps
Chaozheng Wu, Jian Chen, Qiaoyu Cao +4
Learning robotic grasps from visual observations is a promising yet challenging task. Recent research shows its great potential by preparing and learning from large-scale synthetic…
Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priors
Lulu Tang, Ke Chen, Chaozheng Wu +3
Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from global configuration of local geometries in a supervised learning manne…