Abstract

This paper focuses on semantic scene completion, a task for producing a complete 3D voxel representation of volumetric occupancy and semantic labels for a scene from a single-view depth map observation. Previous work has considered scene completion and semantic labeling of depth maps separately. However, we observe that these two problems are tightly intertwined. To leverage the coupled nature of these two tasks, we introduce the semantic scene completion network (SSCNet), an end-to-end 3D convolutional network that takes a single depth image as input and simultaneously outputs occupancy and semantic labels for all voxels in the camera view frustum. Our network uses a dilation-based 3D context module to efficiently expand the receptive field and enable 3D context learning. To train our network, we construct SUNCG - a manually created large-scale dataset of synthetic 3D scenes with dense volumetric annotations. Our experiments demonstrate that the joint model outperforms methods addressing each task in isolation and outperforms alternative approaches on the semantic scene completion task.

Video

Paper

Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, Thomas Funkhouser Semantic Scene Completion from a Single Depth Image CVPR 2017 Oral

Code

github.com/shurans/sscnet

Citation

@article{song2016ssc,
  author     = {Song, Shuran and Yu, Fisher  and Zeng, Andy and Chang, Angel X and Savva, Manolis and Funkhouser, Thomas},
  title      = {Semantic Scene Completion from a Single Depth Image},
  journal 	 = {Proceedings of 30th IEEE Conference on Computer Vision and Pattern Recognition},
  year       = {2017},
}