mlsanityData to 7 Oct 2026

Ray-ONet: Efficient 3D Reconstruction From A Single RGB Image

arXiv 2107.01899 ↗First posted 5 Jul 2021cs.CVcited by 6

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Abstract (arXiv)

We propose Ray-ONet to reconstruct detailed 3D models from monocular images efficiently. By predicting a series of occupancy probabilities along a ray that is back-projected from a pixel in the camera coordinate, our method Ray-ONet improves the reconstruction accuracy in comparison with Occupancy Networks (ONet), while reducing the network inference complexity to O(N2). As a result, Ray-ONet achieves state-of-the-art performance on the ShapeNet benchmark with more than 20× speed-up at 1283 resolution and maintains a similar memory footprint during inference.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 30 of its 35 references that are in the map. First posted on arXiv 5 Jul 2021.

Builds on

30 of 35 references.

References · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  4. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  5. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  6. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  7. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  8. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  9. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
  10. 2020Convolutional Occupancy Networkscited by 760
  11. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 649
  12. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 587
18 more
  1. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  2. 2015Deep Metric Learning via Lifted Structured Feature Embeddingcited by 449
  3. 2019DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstructioncited by 332
  4. 2019What Do Single-view 3D Reconstruction Networks Learn?cited by 259
  5. 2018Pix3D: Dataset and Methods for Single-Image 3D Shape Modelingcited by 218
  6. 2017Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputscited by 189
  7. 2016Learning a Predictable and Generative Vector Representation for Objectscited by 173
  8. 2019Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Imagescited by 164
  9. 2017Learning Efficient Point Cloud Generation for Dense 3D Object Reconstructioncited by 133
  10. 2018Learning Shape Priors for Single-View 3D Completion and Reconstructioncited by 68
  11. 2020FroDO: From Detections to 3D Objectscited by 51
  12. 2020CoReNet: Coherent 3D scene reconstruction from a single RGB imagecited by 45
  13. 2018Pixels, voxels, and views: A study of shape representations for single view 3D object shape predictioncited by 27
  14. 2020Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetrycited by 24
  15. 2020Coupling Explicit and Implicit Surface Representations for Generative 3D Modelingcited by 19
  16. 2020SeqXY2SeqZ: Structure Learning for 3D Shapes by Sequentially Predicting 1D Occupancy Segments From 2D Coordinatescited by 17
  17. 2020On the generalization of learning-based 3D reconstructioncited by 12
  18. 2020DmifNet:3D Shape Reconstruction Based on Dynamic Multi-Branch Information Fusioncited by 2

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 1.

6 papers.

  1. 2023NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotationscited by 53
  2. 2024TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshescited by 20
  3. 2022SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth Samplingcited by 14
  4. 2022VPFusion: Joint 3D Volume and Pixel-Aligned Feature Fusion for Single and Multi-view 3D Reconstructioncited by 3
  5. 2024Robust Shape Fitting for 3D Scene Abstractioncited by 3
  6. 2025X2BR: High-Fidelity 3D Bone Reconstruction from a Planar X-Ray Image with Hybrid Neural Implicit Methodscited by 0