M
Minghua Liu
Researcher at University of California, San Diego
Publications - 24
Citations - 746
Minghua Liu 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 6, co-authored 14 publications receiving 257 citations. Previous affiliations of Minghua Liu include Tsinghua University & University of California.
Papers
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Journal ArticleDOI
Morphing and Sampling Network for Dense Point Cloud Completion
TL;DR: This work proposes a novel approach to complete the partial point cloud in two stages, which outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).
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.
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.
Proceedings Article
Task and Path Planning for Multi-Agent Pickup and Delivery
TL;DR: Two novel offline Multi-Agent Pickup-and-Delivery algorithms are presented that improve a state-of-the-art online MAPD algorithm with respect to task planning, path planning, and deadlock avoidance for the offline MAPD problem.
Posted Content
Morphing and Sampling Network for Dense Point Cloud Completion
TL;DR: Wang et al. as mentioned in this paper proposed a joint loss function to guide the distribution of the points in a 3D point cloud, which outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).