mlsanityData to 7 Oct 2026

Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks

arXiv 1604.03650 ↗First posted 13 Apr 2016cs.CVcited by 72

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Abstract (arXiv)

As 3D movie viewing becomes mainstream and Virtual Reality (VR) market emerges, the demand for 3D contents is growing rapidly. Producing 3D videos, however, remains challenging. In this paper we propose to use deep neural networks for automatically converting 2D videos and images to stereoscopic 3D format. In contrast to previous automatic 2D-to-3D conversion algorithms, which have separate stages and need ground truth depth map as supervision, our approach is trained end-to-end directly on stereo pairs extracted from 3D movies. This novel training scheme makes it possible to exploit orders of magnitude more data and significantly increases performance. Indeed, Deep3D outperforms baselines in both quantitative and human subject evaluations.

Key facts. Cited by 72 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Apr 2016.

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Citing papers per year: 2021: 20; 2022: 17; 2023: 10; 2024: 9; 2025: 10; 2026: 6.

72 papers.

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  1. 2021The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depthcited by 190
  2. 2021MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesiscited by 133
  3. 2021Geometry-Free View Synthesis: Transformers and no 3D Priorscited by 99
  4. 2022LocalBins: Improving Depth Estimation by Learning Local Distributionscited by 92
  5. 2022OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusioncited by 60
  6. 2024StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videoscited by 36
  7. 2022BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimationcited by 35
  8. 2023SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimatescited by 35
  9. 2021Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimationcited by 27
  10. 2023On the Importance of Accurate Geometry Data for Dense 3D Vision Taskscited by 26
  11. 2021Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervisioncited by 23
  12. 2024SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrixcited by 19
38 more
  1. 2022Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regressioncited by 18
  2. 2023MACARONS: Mapping And Coverage Anticipation with RGB Online Self-Supervisioncited by 18
  3. 2022Depth Map Decomposition for Monocular Depth Estimationcited by 16
  4. 2024Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Surveycited by 15
  5. 2021Stereo Matching by Self-supervision of Multiscopic Visioncited by 14
  6. 2021GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Networkcited by 12
  7. 2022Crafting Monocular Cues and Velocity Guidance for Self-Supervised Multi-Frame Depth Learningcited by 12
  8. 2022A Survey on RGB-D Datasetscited by 11
  9. 2022Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimationcited by 10
  10. 2022CroMo: Cross-Modal Learning for Monocular Depth Estimationcited by 10
  11. 2025M2SVid: End-to-End Inpainting and Refinement for Monocular-to-Stereo Video Conversioncited by 10
  12. 2025Mono2Stereo: A Benchmark and Empirical Study for Stereo Conversioncited by 9
  13. 2021Unsupervised Novel View Synthesis from a Single Imagecited by 8
  14. 2022Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Drivingcited by 8
  15. 2024ImmersePro: End-to-End Stereo Video Synthesis Via Implicit Disparity Learningcited by 8
  16. 2021On the Sins of Image Synthesis Loss for Self-supervised Depth Estimationcited by 7
  17. 2024DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applicationscited by 7
  18. 2022Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth Estimationcited by 6
  19. 2025Eye2Eye: A Simple Approach for Monocular-to-Stereo Video Synthesiscited by 6
  20. 2021Advancing Autonomous Driving: DepthSense with Radar and Spatial Attentioncited by 5
  21. 2023Virtual Occlusions Through Implicit Depthcited by 5
  22. 2024DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generationcited by 5
  23. 2021Traffic-Net: 3D Traffic Monitoring Using a Single Cameracited by 4
  24. 2023Single-View View Synthesis with Self-Rectified Pseudo-Stereocited by 4
  25. 2021Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Datacited by 3
  26. 2021Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernelscited by 3
  27. 2022Creating a Forensic Database of Shoeprints from Online Shoe Tread Photoscited by 3
  28. 2022How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecastingcited by 3
  29. 2024Less Cybersickness, Please: Demystifying and Detecting Stereoscopic Visual Inconsistencies in Virtual Reality Appscited by 3
  30. 2025DMS:Diffusion-Based Multi-Baseline Stereo Generation for Improving Self-Supervised Depth Estimationcited by 3
  31. 2025Elastic3D: Controllable Stereo Video Conversion with Guided Latent Decodingcited by 3
  32. 2025StereoPilot: Learning Unified and Efficient Stereo Conversion via Generative Priorscited by 3
  33. 2022Self-supervised 360circ Room Layout Estimationcited by 2
  34. 2022Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encodercited by 2
  35. 2022Positional Information is All You Need: A Novel Pipeline for Self-Supervised SVDE from Videoscited by 2
  36. 2023Learning Internal Representations of 3D Transformations from 2D Projected Inputscited by 2
  37. 2023LoLep: Single-View View Synthesis with Locally-Learned Planes and Self-Attention Occlusion Inferencecited by 2
  38. 2023ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimationcited by 2

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