2.1k citations · 2.3k across the 4 of their papers we have counts for
6 papers
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…
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Charles R. Qi, Li Yi, Hao Su +1
Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by th…
Learning Hierarchical Shape Segmentation and Labeling from Online Repositories
Li Yi, Leonidas Guibas, Aaron Hertzmann +3
We propose a method for converting geometric shapes into hierarchically segmented parts with part labels. Our key idea is to train category-specific models from the scene graphs an…
Volumetric and Multi-View CNNs for Object Classification on 3D Data
Charles R. Qi, Hao Su, Matthias Niessner +3
3D shape models are becoming widely available and easier to capture, making available 3D information crucial for progress in object classification. Current state-of-the-art methods…
Pose Estimation Based on 3D Models
Chuiwen Ma, Hao Su, Liang Shi
In this paper, we proposed a pose estimation system based on rendered image training set, which predicts the pose of objects in real image, with knowledge of object category and ti…
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
Hao Su, Charles R. Qi, Yangyan Li +1
Object viewpoint estimation from 2D images is an essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, a…