H
Hao Su
Researcher at University of California, San Diego
Publications - 364
Citations - 82843
Hao Su is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 57, co-authored 302 publications receiving 55902 citations. Previous affiliations of Hao Su include Philips & Jiangxi University of Science and Technology.
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
TL;DR: PointNet as discussed by the authors proposes a novel type of neural network that directly consumes point clouds and well respects the permutation invariance of points in the input, which provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.
Proceedings ArticleDOI
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
TL;DR: A high-level image representation, called the Object Bank, is proposed, where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors, blind to the testing dataset or visual task.
Journal ArticleDOI
A scalable active framework for region annotation in 3D shape collections
Li Yi,Vladimir G. Kim,Duygu Ceylan,I-Chao Shen,Mengyan Yan,Hao Su,Cewu Lu,Qixing Huang,Alla Sheffer,Leonidas J. Guibas +9 more
TL;DR: This work proposes a novel active learning method capable of enriching massive geometric datasets with accurate semantic region annotations, and demonstrates that incorporating verification of all produced labelings within this unified objective improves both accuracy and efficiency of the active learning procedure.
Proceedings ArticleDOI
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
TL;DR: A scalable and overfit-resistant image synthesis pipeline, together with a novel CNN specifically tailored for the viewpoint estimation task, is proposed that can significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark.
Posted Content
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared.