We present an approach for joint inference of 3D scene structure and semantic labeling for monocular video. Starting with monocular image stream, our framework produces a 3D volumetric semantic + occupancy map, which is much more useful than a series of 2D semantic label images or a sparse point cloud produced by traditional semantic segmentation 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 structure and temporally consistent semantic segmentation for difficult, large scale, forward moving monocular image sequences.


ECCV 2014

Abhijit Kundu, Yin Li, Frank Daellert, Fuxin Li and James M. Rehg. Joint Semantic Segmentation and 3D Reconstruction from Monocular Video. ECCV 2014


Joint 3D Reconstruction & Semantic Segmentation Map


Under Construction

While working on this paper we labelled several images from the KITTI dataset for semantic segmentation. You can download the annotations from here.


This work was supported in part by ARO-MURI award W911NF-11-1-004.


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