2.1k citations · 2.4k across the 8 of their papers we have counts for
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Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
He Wang, Zetian Jiang, Li Yi +3
In this paper, we examine the long-neglected yet important effects of point sampling patterns in point cloud GANs. Through extensive experiments, we show that sampling-insensitive…
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…
DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces
Jun Gao, Chengcheng Tang, Vignesh Ganapathi-Subramanian +3
Reconstruction of geometry based on different input modes, such as images or point clouds, has been instrumental in the development of computer aided design and computer graphics.…
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding
Kaichun Mo, Shilin Zhu, Angel X. Chang +4
We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information. Our dataset consists of 573,5…
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
Minhyuk Sung, Hao Su, Ronald Yu +1
Various 3D semantic attributes such as segmentation masks, geometric features, keypoints, and materials can be encoded as per-point probe functions on 3D geometries. Given a collec…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…