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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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Proceedings ArticleDOI
Scan2CAD: Learning CAD Model Alignment in RGB-D Scans
TL;DR: This work designs a novel 3D CNN architecture that learns a joint embedding between real and synthetic objects, and from this predicts a correspondence heatmap, which forms a variational energy minimization that aligns a given set of CAD models to the reconstruction.
Proceedings ArticleDOI
Eviza: A Natural Language Interface for Visual Analysis
TL;DR: Eviza provides a natural language interface for an interactive query dialog with an existing visualization rather than starting from a blank sheet and asking closed-ended questions that return a single text answer or static visualization.
Journal ArticleDOI
Deep convolutional priors for indoor scene synthesis
TL;DR: This work presents a convolutional neural network based approach for indoor scene synthesis that generates scenes that are preferred over the baselines, and in some cases are equally preferred to human-created scenes.
Journal ArticleDOI
PlanIT: planning and instantiating indoor scenes with relation graph and spatial prior networks
TL;DR: A new framework for interior scene synthesis that combines a high-level relation graph representation with spatial prior neural networks, and generates scenes of comparable quality to those generated by prior approaches, while also providing the modeling flexibility of the intermediate relationship graph representation.
Proceedings ArticleDOI
Learning Spatial Knowledge for Text to 3D Scene Generation
TL;DR: The main innovation of this work is to show how to augment explicit constraints with learned spatial knowledge to infer missing objects and likely layouts for the objects in the scene.