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Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View
Shuran Song, Andy Zeng, Angel X. Chang +3
We present Im2Pano3D, a convolutional neural network that generates a dense prediction of 3D structure and a probability distribution of semantic labels for a full 360 panoramic vi…
MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
Manolis Savva, Angel X. Chang, Alexey Dosovitskiy +2
We present MINOS, a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. The simulator leverages large…
Matterport3D: Learning from RGB-D Data in Indoor Environments
Angel Chang, Angela Dai, Thomas Funkhouser +6
Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a re…
Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes
Kyle Genova, Manolis Savva, Angel X. Chang +1
The use of rendered images, whether from completely synthetic datasets or from 3D reconstructions, is increasingly prevalent in vision tasks. However, little attention has been giv…
SceneSuggest: Context-driven 3D Scene Design
Manolis Savva, Angel X. Chang, Maneesh Agrawala
We present SceneSuggest: an interactive 3D scene design system providing context-driven suggestions for 3D model retrieval and placement. Using a point-and-click metaphor we specif…
SceneSeer: 3D Scene Design with Natural Language
Angel X. Chang, Mihail Eric, Manolis Savva +1
Designing 3D scenes is currently a creative task that requires significant expertise and effort in using complex 3D design interfaces. This effortful design process starts in stark…
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
Angela Dai, Angel X. Chang, Manolis Savva +3
A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very l…