2.1k citations · 2.2k across the 6 of their papers we have counts for
12 papers · 1 filter
Curriculum DeepSDF
Yueqi Duan, Haidong Zhu, He Wang +3
When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions. In this…
Category-Level Articulated Object Pose Estimation
Xiaolong Li, He Wang, Li Yi +3
This project addresses the task of category-level pose estimation for articulated objects from a single depth image. We present a novel category-level approach that correctly accom…
StructEdit: Learning Structural Shape Variations
Kaichun Mo, Paul Guerrero, Li Yi +4
Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape…
AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri +3
We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds. Differently from the familiar single-insta…
GeoNet: Deep Geodesic Networks for Point Cloud Analysis
Tong He, Haibin Huang, Li Yi +4
Surface-based geodesic topology provides strong cues for object semantic analysis and geometric modeling. However, such connectivity information is lost in point clouds. Thus we in…
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
Li Yi, Wang Zhao, He Wang +2
We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data. Instead of treating object proposal a…