Learning to Exploit Stability for 3D Scene Parsing


The Physical Stability Model (PSM) consists of three modules. A primitive prediction module predicts each object in a scene as a set of cuboid primitives, a layout prediction module predicts the walls surrounding a scene, and a physics stability module provides feedback on the stability of the predictions. We show our end-to-end primitive prediction network (Prim R-CNN) for the primitive prediction module.


Human scene understanding uses a variety of visual and non-visual cues to perform inference on object types, poses, and relations. Physics is a rich and universal cue that we exploit to enhance scene understanding. In this paper, we integrate the physical cue of stability into the learning process by looping in a physics engine into bottom-up recognition models, and apply it to the problem of 3D scene parsing. We first show that applying physics supervision to an existing scene understanding model increases performance, produces more stable predictions, and allows training to an equivalent performance level with fewer annotated training examples. We then present a novel architecture for 3D scene parsing named Prim R-CNN, learning to predict bounding boxes as well as their 3D size, translation, and rotation. With physics supervision, Prim R-CNN outperforms existing scene understanding approaches on this problem. Finally, we show that finetuning with physics supervision on unlabeled real images improves real domain transfer of models training on synthetic data.