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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…
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
Kaichun Mo, Paul Guerrero, Li Yi +4
The ability to generate novel, diverse, and realistic 3D shapes along with associated part semantics and structure is central to many applications requiring high-quality 3D assets…
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…