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View-volume Network for Semantic Scene Completion from a Single Depth Image

arXiv 1806.05361 ↗First posted 14 Jun 2018cs.CVcited by 26

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Abstract (arXiv)

We introduce a View-Volume convolutional neural network (VVNet) for inferring the occupancy and semantic labels of a volumetric 3D scene from a single depth image. The VVNet concatenates a 2D view CNN and a 3D volume CNN with a differentiable projection layer. Given a single RGBD image, our method extracts the detailed geometric features from the input depth image with a 2D view CNN and then projects the features into a 3D volume according to the input depth map via a projection layer. After that, we learn the 3D context information of the scene with a 3D volume CNN for computing the result volumetric occupancy and semantic labels. With combined 2D and 3D representations, the VVNet efficiently reduces the computational cost, enables feature extraction from multi-channel high resolution inputs, and thus significantly improves the result accuracy. We validate our method and demonstrate its efficiency and effectiveness on both synthetic SUNCG and real NYU dataset.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Jun 2018.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 6; 2024: 2; 2025: 4; 2026: 1.

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  1. 2023Symphonize 3D Semantic Scene Completion with Contextual Instance Queriescited by 90
  2. 20213D Semantic Scene Completion: a Surveycited by 74
  3. 2023AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformerscited by 67
  4. 2021Semantic Scene Completion via Integrating Instances and Scene in-the-Loopcited by 55
  5. 2022MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environmentscited by 40
  6. 2021Indoor Scene Generation from a Collection of Semantic-Segmented Depth Imagescited by 27
  7. 2021Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspectivecited by 22
  8. 2022Point Scene Understanding via Disentangled Instance Mesh Reconstructioncited by 20
  9. 2022Learning Local Displacements for Point Cloud Completioncited by 16
  10. 2023Semantic Scene Completion with Cleaner Selfcited by 12
  11. 2022CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusioncited by 11
  12. 2021IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancementcited by 9
14 more
  1. 2021Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplificationcited by 9
  2. 2021Data Augmented 3D Semantic Scene Completion with 2D Segmentation Priorscited by 9
  3. 2025PointSea: Point Cloud Completion via Self-structure Augmentationcited by 9
  4. 2023SSR-2D: Semantic 3D Scene Reconstruction from 2D Imagescited by 7
  5. 2022Point Cloud Scene Completion with Joint Color and Semantic Estimation from Single RGB-D Imagecited by 6
  6. 2025VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene Completioncited by 6
  7. 2023CVSformer: Cross-View Synthesis Transformer for Semantic Scene Completioncited by 4
  8. 2024Unleashing Network Potentials for Semantic Scene Completioncited by 4
  9. 2023Real-time 3D Semantic Scene Completion Via Feature Aggregation and Conditioned Predictioncited by 3
  10. 2025Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidancecited by 3
  11. 2025Vision-based 3D Semantic Scene Completion via Capture Dynamic Representationscited by 2
  12. 2026MGPC: Multimodal Network for Generalizable Point Cloud Completion With Modality Dropout and Progressive Decodingcited by 2
  13. 2022Unsigned Distance Field as an Accurate 3D Scene Representation for Neural Scene Completioncited by 1
  14. 2024A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structurescited by 0