The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Differentiable Monte Carlo ray tracing through edge sampling
Open accessAbstract
Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates. We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision. We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
(2017) · Cited by 11,625
A morphable model for the synthesis of 3D faces
Blanz, V., & Vetter, T. (1999) · Proceedings of the 26th annual conference on Computer graphics and interactive techniques - SIGGRAPH '99 · doi:10.1145/311535.311556 · Cited by 4,710
Kajiya, J. T. (1986) · ACM SIGGRAPH Computer Graphics · doi:10.1145/15886.15902 · Cited by 2,238
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition
Griewank, A., & Walther, A. (2008) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898717761 · Cited by 1,529
The Analysis of Linear Partial Differential Operators I: Distribution Theory and Fourier Analysis
Hörmander, L. (2003) · Classics in Mathematics · doi:10.1007/978-3-642-61497-2 · Cited by 1,345
Kato, H., Ushiku, Y., & Harada, T. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00411 · Cited by 954
Shape, Illumination, and Reflectance from Shading
Barron, J. T., & Malik, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2377712 · Cited by 656
Optimally combining sampling techniques for Monte Carlo rendering
Veach, E., & Guibas, L. J. (1995) · Proceedings of the 22nd annual conference on Computer graphics and interactive techniques - SIGGRAPH '95 · doi:10.1145/218380.218498 · Cited by 616
Cited by
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_24 · Cited by 5,351
Soft Rasterizer: A Differentiable Renderer for Image-Based 3D Reasoning
Liu, S., Chen, W., Li, T., & Li, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00780 · Cited by 641
Self-citation3D Morphable Face Models—Past, Present, and Future
Egger, B., Smith, W. A. P., Tewari, A., Wuhrer, S., Zollhoefer, M., Beeler, T., Bernard, F., Bolkart, T., Kortylewski, A., Romdhani, S., et al. (2020) · ACM Transactions on Graphics · doi:10.1145/3395208 · Cited by 448
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., & Yang, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00768 · Cited by 340
ARCH: Animatable Reconstruction of Clothed Humans
Huang, Z., Xu, Y., Lassner, C., Li, H., & Tung, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00316 · Cited by 341
Metadata
- Authors
- Li, T.-M., Aittala, M., Durand, F., & Lehtinen, J.
- Venue
- ACM Transactions on Graphics
- Published
- 2018-12-04
- Volume
- 37
- Issue
- 6
- Pages
- 1-11
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2902812770
- Cited by
- 428
- References
- 24
- FWCI
- 97.95
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Differentiable Monte Carlo ray tracing through edge sampling. https://app.cochat.ai/papers/10.1145/3272127.3275109
- The paper
- Li, T.-M., Aittala, M., Durand, F., & Lehtinen, J. (2018). Differentiable Monte Carlo ray tracing through edge sampling. ACM Transactions on Graphics. https://doi.org/10.1145/3272127.3275109
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
