Abstract

We present an approach for joint inference of 3D scene struc- ture and semantic labeling for monocular video. Starting with monocular image stream, our framework produces a 3D volumetric semantic + occu- pancy map, which is much more useful than a series of 2D semantic label images or a sparse point cloud produced by traditional semantic segmen- tation and Structure from Motion(SfM) pipelines respectively. We derive a Conditional Random Field (CRF) model defined in the 3D space, that jointly infers the semantic category and occupancy for each voxel. Such a joint inference in the 3D CRF paves the way for more informed priors and constraints, which is otherwise not possible if solved separately in their traditional frameworks. We make use of class specific semantic cues that constrain the 3D structure in areas, where multiview constraints are weak. Our model comprises of higher order factors, which helps when the depth is unobservable. We also make use of class specific semantic cues to reduce either the degree of such higher order factors, or to approximately model them with unaries if possible. We demonstrate improved 3D struc- ture and temporally consistent semantic segmentation for difficult, large scale, forward moving monocular image sequences.