Papers › Semantic Scene Completion from a Single Depth Image

Semantic Scene Completion from a Single Depth Image

28 Nov 2016CVPR 2017 7arXiv:1611.08974archive 2025-07-28

Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, Thomas Funkhouser

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.

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Tasks

3D Semantic Scene Completion

Datasets

Introduced by this paper, per the archive.

SUNCG

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Semantic Scene Completion KITTI-360 SSCNet mIoU 16.95 #2 of 7 Archive leaderboard report
3D Semantic Scene Completion NYUv2 SSCNet (SUNCG pretraining) mIoU 30.5 #19 of 28 Archive leaderboard report
3D Semantic Scene Completion NYUv2 SSCNet mIoU 24.7 #27 of 28 Archive leaderboard report
3D Semantic Scene Completion SemanticKITTI SSCNet (reported in LMSCNet) mIoU 16.1 #13 of 20 Archive leaderboard report
3D Semantic Scene Completion SemanticKITTI SSCNet-full (reported in LMSCNet) mIoU 16.1 #14 of 20 Archive leaderboard report

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