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.
Papers
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Journal ArticleDOI
Tumour sensitization via the extended intratumoural release of a STING agonist and camptothecin from a self-assembled hydrogel.
Feihu Wang,Hao Su,Dongqing Xu,Wenbing Dai,Weijie Zhang,Weijie Zhang,Zongyuan Wang,Caleb F. Anderson,Mengzhen Zheng,Richard Oh,Fengyi Wan,Fengyi Wan,Honggang Cui,Honggang Cui +13 more
TL;DR: In multiple mouse models of murine tumours, a single low dose of the STING agonist led to tumour regression and increased animal survival, and to long-term immunological memory and systemic immune surveillance, which protected the mice against tumour recurrence and the formation of metastases.
Posted Content
SAPIEN: A SimulAted Part-based Interactive ENvironment
Fanbo Xiang,Yuzhe Qin,Kaichun Mo,Yikuan Xia,Hao Zhu,Fangchen Liu,Minghua Liu,Hanxiao Jiang,Yifu Yuan,He Wang,Li Yi,Angel X. Chang,Leonidas J. Guibas,Hao Su +13 more
TL;DR: SAPIEN is a realistic and physics-rich simulated environment that hosts a large-scale set of articulated objects that enables various robotic vision and interaction tasks that require detailed part-level understanding and hopes it will open research directions yet to be explored.
Posted Content
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
TL;DR: Zhang et al. as mentioned in this paper proposed a framework to combine render-based image synthesis and CNNs for object viewpoint estimation from 2D images, which can be well exploited by deep CNNs with a high learning capacity.
Posted Content
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
TL;DR: StructureNet is introduced, a hierarchical graph network which can directly encode shapes represented as such n-ary graphs, and can be robustly trained on large and complex shape families and used to generate a great diversity of realistic structured shape geometries.
Proceedings ArticleDOI
SAPIEN: A SimulAted Part-Based Interactive ENvironment
Fanbo Xiang,Yuzhe Qin,Kaichun Mo,Yikuan Xia,Hao Zhu,Fangchen Liu,Minghua Liu,Hanxiao Jiang,Yifu Yuan,He Wang,Li Yi,Angel X. Chang,Leonidas J. Guibas,Hao Su +13 more
TL;DR: SAPIEN as mentioned in this paper is a realistic and physics-rich simulated environment that hosts a large-scale set of articulated objects for part detection and motion attribute recognition, as well as demonstrate robotic interaction tasks using heuristic approaches and reinforcement learning algorithms.