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Angel X. Chang
Researcher at Simon Fraser University
Publications - 94
Citations - 16297
Angel X. Chang is an academic researcher from Simon Fraser University. The author has contributed to research in topics: Computer science & Natural language. The author has an hindex of 35, co-authored 77 publications receiving 11135 citations. Previous affiliations of Angel X. Chang include Princeton University & Stanford University.
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ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang,Thomas Funkhouser,Leonidas J. Guibas,Pat Hanrahan,Qixing Huang,Zimo Li,Silvio Savarese,Manolis Savva,Shuran Song,Hao Su,Jianxiong Xiao,Li Yi,Fisher Yu +12 more
TL;DR: ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy, a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations.
Proceedings ArticleDOI
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
TL;DR: This work introduces ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations, and shows that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks.
Proceedings ArticleDOI
Semantic Scene Completion from a Single Depth Image
TL;DR: The semantic scene completion network (SSCNet) is introduced, an end-to-end 3D convolutional network that takes a single depth image as input and simultaneously outputs occupancy and semantic labels for all voxels in the camera view frustum.
Proceedings ArticleDOI
Matterport3D: Learning from RGB-D Data in Indoor Environments
Angel X. Chang,Angela Dai,Thomas Funkhouser,Maciej Halber,Matthias NieBner,Manolis Savva,Shuran Song,Andy Zeng,Yinda Zhang +8 more
TL;DR: Matterport3D as discussed by the authors is a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400 images of 90 building-scale scenes.
Posted Content
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
TL;DR: The ScanNet dataset as discussed by the authors contains 2.5M RGB-D views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations.