Semantic Scene Completion from a Single Depth Image

Citations Over TimeTop 1% of 2017 papers

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 largescale 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. The dataset and code is available at http://sscnet.cs.princeton.edu.

Related Papers

• → Divided Voxels: an efficient algorithm for interactive cutting of deformable objects(2020)11 cited
• → VOXELISATION AND VOXEL MANAGEMENT OPTIONS IN UNITY3D(2022)6 cited
• → Weighted voxel(2018)9 cited
• → Surface scan voxel coloring: A surface scan algorithm for scene reconstruction by voxel coloring(2010)
• → Line-Based Structure for Voxel Model(2021)