mlsanityData to 7 Oct 2026

CIRCLE: Convolutional Implicit Reconstruction and Completion for Large-scale Indoor Scene

arXiv 2111.12905 ↗First posted 25 Nov 2021cs.CV cs.AI cs.GRcited by 4

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Abstract (arXiv)

We present CIRCLE, a framework for large-scale scene completion and geometric refinement based on local implicit signed distance functions. It is based on an end-to-end sparse convolutional network, CircNet, that jointly models local geometric details and global scene structural contexts, allowing it to preserve fine-grained object detail while recovering missing regions commonly arising in traditional 3D scene data. A novel differentiable rendering module enables test-time refinement for better reconstruction quality. Extensive experiments on both real-world and synthetic datasets show that our concise framework is efficient and effective, achieving better reconstruction quality than the closest competitor while being 10-50x faster.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 39 of its 63 references that are in the map. First posted on arXiv 25 Nov 2021.

Builds on

39 of 63 references.

References · 39

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  4. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
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  11. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  12. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
27 more
  1. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 550
  2. 2021iMAP: Implicit Mapping and Positioning in Real-Timecited by 476
  3. 2017Neural 3D Mesh Renderercited by 437
  4. 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
  5. 2019Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoningcited by 420
  6. 2020Local Implicit Grid Representations for 3D Scenescited by 372
  7. 2020Implicit Functions in Feature Space for 3D Shape Reconstruction and Completioncited by 343
  8. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
  9. 2021Neural RGB-D Surface Reconstructioncited by 312
  10. 2019Learning Shape Templates with Structured Implicit Functionscited by 242
  11. 2021NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Videocited by 241
  12. 2017Submanifold Sparse Convolutional Networkscited by 223
  13. 2019Texture Fields: Learning Texture Representations in Function Spacecited by 210
  14. 2019BSP-Net: Generating Compact Meshes via Binary Space Partitioningcited by 205
  15. 2019DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracingcited by 179
  16. 2019SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimizationcited by 159
  17. 2016Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planningcited by 148
  18. 2021TransformerFusion: Monocular RGB Scene Reconstruction using Transformerscited by 107
  19. 2017ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scanscited by 103
  20. 2019SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scanscited by 77
  21. 2020DI-Fusion: Online Implicit 3D Reconstruction with Deep Priorscited by 70
  22. 2020RoutedFusion: Learning Real-time Depth Map Fusioncited by 52
  23. 2020NeuralFusion: Online Depth Fusion in Latent Spacecited by 46
  24. 2020SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scanscited by 34
  25. 2020Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completioncited by 33
  26. 2017Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of Viewcited by 25
  27. 2021NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesiscited by 7

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.

4 papers.

  1. 2023Neural Kernel Surface Reconstructioncited by 102
  2. 2022MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priorscited by 17
  3. 2025ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Graspingcited by 13
  4. 2024Zero-Shot Multi-Object Scene Completioncited by 9