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While deep neural networks have led to human-level performance on computervision tasks, they have yet to demonstrate similar gains for holistic sceneunderstanding. In particular, 3D context has been shown to be an extremelyimportant cue for scene understanding - yet very little research has been doneon integrating context information with deep models. This paper presents anapproach to embed 3D context into the topology of a neural network trained toperform holistic scene understanding. Given a depth image depicting a 3D scene,our network aligns the observed scene with a predefined 3D scene template, andthen reasons about the existence and location of each object within the scenetemplate. In doing so, our model recognizes multiple objects in a singleforward pass of a 3D convolutional neural network, capturing both global sceneand local object information simultaneously. To create training data for this3D network, we generate partly hallucinated depth images which are rendered byreplacing real objects with a repository of CAD models of the same objectcategory. Extensive experiments demonstrate the effectiveness of our algorithmcompared to the state-of-the-arts. Source code and data are available athttp://deepcontext.cs.princeton.edu.

DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding