2.9k citations · 10.7k across the 136 of their papers we have counts for
12 papers · 1 filter
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…
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…
Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies
Alexander Sax, Bradley Emi, Amir R. Zamir +3
How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. delivering a package)? We study t…
TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes
Jingwei Huang, Haotian Zhang, Li Yi +3
We introduce, TextureNet, a neural network architecture designed to extract features from high-resolution signals associated with 3D surface meshes (e.g., color texture maps). The…
Supervised Fitting of Geometric Primitives to 3D Point Clouds
Lingxiao Li, Minhyuk Sung, Anastasia Dubrovina +2
Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized 3D data and high-level structural information on the underlying 3D shapes. As such, it…
Deep Part Induction from Articulated Object Pairs
Li Yi, Haibin Huang, Difan Liu +3
Object functionality is often expressed through part articulation -- as when the two rigid parts of a scissor pivot against each other to perform the cutting function. Such articul…