mlsanityData to 7 Oct 2026

Differentiable Rendering: A Survey

arXiv 2006.12057 ↗First posted 22 Jun 2020cs.CV cs.GRcited by 123

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Abstract (arXiv)

Deep neural networks (DNNs) have shown remarkable performance improvements on vision-related tasks such as object detection or image segmentation. Despite their success, they generally lack the understanding of 3D objects which form the image, as it is not always possible to collect 3D information about the scene or to easily annotate it. Differentiable rendering is a novel field which allows the gradients of 3D objects to be calculated and propagated through images. It also reduces the requirement of 3D data collection and annotation, while enabling higher success rate in various applications. This paper reviews existing literature and discusses the current state of differentiable rendering, its applications and open research problems.

Key facts. Cited by 123 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 22; 2022: 34; 2023: 22; 2024: 21; 2025: 9; 2026: 15.

123 papers.

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  1. 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
  2. 2022Decomposing NeRF for Editing via Feature Field Distillationcited by 370
  3. 2022NeILF: Neural Incident Light Field for Physically-based Material Estimationcited by 105
  4. 2021Unsupervised Discovery of Object Radiance Fieldscited by 100
  5. 2021Gradients are Not All You Needcited by 82
  6. 2021Learning Signed Distance Field for Multi-view Surface Reconstructioncited by 76
  7. 2021Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Renderingcited by 71
  8. 2024Advances in 3D Generation: A Surveycited by 62
  9. 2021Paint Transformer: Feed Forward Neural Painting with Stroke Predictioncited by 61
  10. 2022A Survey of Non-Rigid 3D Registrationcited by 59
  11. 2022Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Inputcited by 49
  12. 2021Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Drivingcited by 45
38 more
  1. 2023RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatarscited by 45
  2. 2022CLIP-Actor: Text-Driven Recommendation and Stylization for Animating Human Meshescited by 41
  3. 2024When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Modelscited by 41
  4. 2021Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Conceptscited by 40
  5. 2022Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimationcited by 34
  6. 2021Recursive-NeRF: An Efficient and Dynamically Growing NeRFcited by 32
  7. 2022DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualizationcited by 32
  8. 2023BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fieldscited by 27
  9. 2022Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistencycited by 26
  10. 2023CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformercited by 24
  11. 20223DFaceShop: Explicitly Controllable 3D-Aware Portrait Generationcited by 22
  12. 2022SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Renderingcited by 22
  13. 2022High-Res Facial Appearance Capture from Polarized Smartphone Imagescited by 21
  14. 2024GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstructioncited by 19
  15. 2023MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shadingcited by 18
  16. 2023Re-ReND: Real-time Rendering of NeRFs across Devicescited by 17
  17. 2025AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletonscited by 17
  18. 2023PAPR: Proximity Attention Point Renderingcited by 16
  19. 2023NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fieldscited by 16
  20. 2025Reconstructing 4D Spatial Intelligence: A Surveycited by 13
  21. 2022GenDR: A Generalized Differentiable Renderercited by 12
  22. 2024Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learningcited by 12
  23. 2023Style2Fab: Functionality-Aware Segmentation for Fabricating Personalized 3D Models with Generative AIcited by 11
  24. 2021Shadow Art Revisited: A Differentiable Rendering Based Approachcited by 10
  25. 2022Plateau-reduced Differentiable Path Tracingcited by 10
  26. 2023NeRF Revisited: Fixing Quadrature Instability in Volume Renderingcited by 10
  27. 2025Materialist: Physically Based Editing Using Single-Image Inverse Renderingcited by 10
  28. 2021ADOP: Approximate Differentiable One-Pixel Point Renderingcited by 9
  29. 2023Image-based Pose Estimation and Shape Reconstruction for Robot Manipulators and Soft, Continuum Robots via Differentiable Renderingcited by 9
  30. 2023Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Priorcited by 9
  31. 2022Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detectioncited by 8
  32. 2022Leveraging Single-View Images for Unsupervised 3D Point Cloud Completioncited by 8
  33. 2021An Effective Loss Function for Generating 3D Models from Single 2D Image without Renderingcited by 7
  34. 2023SSR-2D: Semantic 3D Scene Reconstruction from 2D Imagescited by 7
  35. 2023DiViNeT: 3D Reconstruction from Disparate Views via Neural Template Regularizationcited by 7
  36. 2024Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimationcited by 7
  37. 2021Physics-based Differentiable Depth Sensor Simulationcited by 6
  38. 2022ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapescited by 6

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