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{
"id": "R1",
"citation": "NVIDIA. *DLSS Technology*. Official description of the current neural-rendering suite. [Official page](https://www.nvidia.com/en-us/geforce/technologies/dlss/).",
"url": "https://www.nvidia.com/en-us/geforce/technologies/dlss/",
"checked": "2026-09-19",
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{
"id": "R2",
"citation": "Michael L. Littman, Richard S. Sutton, Satinder Singh. *Predictive Representations of State*. Advances in Neural Information Processing Systems 14, 2001. Author list checked against the paper PDF; the proceedings landing metadata omits Singh. [Paper](https://papers.nips.cc/paper_files/paper/2001/file/1e4d36177d71bbb3558e43af9577d70e-Paper.pdf).",
"url": "https://papers.nips.cc/paper_files/paper/2001/file/1e4d36177d71bbb3558e43af9577d70e-Paper.pdf",
"checked": "2026-09-19",
"source_type": "primary"
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{
"id": "R3",
"citation": "Siddharth Joshi and Stephen Boyd. *Sensor Selection via Convex Optimization*. IEEE Transactions on Signal Processing 57(2), 451-462, 2009. [Author publication page](https://web.stanford.edu/~boyd/papers/sensor_selection.html).",
"url": "https://web.stanford.edu/~boyd/papers/sensor_selection.html",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R4",
"citation": "Christoph Schied et al. *Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path-Traced Global Illumination*. High Performance Graphics, 2017. [Publication](https://research.nvidia.com/publication/2017-07_spatiotemporal-variance-guided-filtering-real-time-reconstruction-path-traced).",
"url": "https://research.nvidia.com/publication/2017-07_spatiotemporal-variance-guided-filtering-real-time-reconstruction-path-traced",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R5",
"citation": "Chakravarty R. Alla Chaitanya et al. *Interactive Reconstruction of Monte Carlo Image Sequences using a Recurrent Denoising Autoencoder*. SIGGRAPH, 2017. [Publication](https://research.nvidia.com/publication/2017-07_interactive-reconstruction-monte-carlo-image-sequences-using-recurrent).",
"url": "https://research.nvidia.com/publication/2017-07_interactive-reconstruction-monte-carlo-image-sequences-using-recurrent",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R6",
"citation": "Thomas Muller, Fabrice Rousselle, Jan Novak, Alexander Keller. *Real-time Neural Radiance Caching for Path Tracing*. ACM Transactions on Graphics, 2021. [Publication](https://research.nvidia.com/publication/2021-06_real-time-neural-radiance-caching-path-tracing).",
"url": "https://research.nvidia.com/publication/2021-06_real-time-neural-radiance-caching-path-tracing",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R7",
"citation": "Benedikt Bitterli et al. *Spatiotemporal reservoir resampling for real-time ray tracing with dynamic direct lighting*. ACM Transactions on Graphics, 2020. [Publication](https://research.nvidia.com/publication/2020-07_spatiotemporal-reservoir-resampling-real-time-ray-tracing-dynamic-direct).",
"url": "https://research.nvidia.com/publication/2020-07_spatiotemporal-reservoir-resampling-real-time-ray-tracing-dynamic-direct",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R8",
"citation": "Zheng Zeng et al. *ReSTIR PG: Path Guiding with Spatiotemporally Resampled Paths*. SIGGRAPH Asia Conference Track, 2025. [Publication](https://research.nvidia.com/labs/rtr/publication/zeng2025restirpg/).",
"url": "https://research.nvidia.com/labs/rtr/publication/zeng2025restirpg/",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R9",
"citation": "Pengpei Hong et al. *Multi-Layer Reservoir Splatting for Temporal Reuse under Disocclusion*. SIGGRAPH Conference Track, 2026. [Publication](https://research.nvidia.com/labs/rtr/publication/hong2026multilayer/).",
"url": "https://research.nvidia.com/labs/rtr/publication/hong2026multilayer/",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R10",
"citation": "Bing Xu et al. *A Generalizable Light Transport 3D Embedding for Global Illumination*. SIGGRAPH Conference Track, 2026. [Publication](https://research.nvidia.com/labs/rtr/publication/xu2026generalizable/).",
"url": "https://research.nvidia.com/labs/rtr/publication/xu2026generalizable/",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R11",
"citation": "Ben Mildenhall et al. *NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis*. ECCV, 2020. [Preprint](https://arxiv.org/abs/2003.08934).",
"url": "https://arxiv.org/abs/2003.08934",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R12",
"citation": "Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, George Drettakis. *3D Gaussian Splatting for Real-Time Radiance Field Rendering*. ACM Transactions on Graphics, 2023. [Preprint](https://arxiv.org/abs/2308.04079).",
"url": "https://arxiv.org/abs/2308.04079",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R13",
"citation": "Thomas Muller, Alex Evans, Christoph Schied, Alexander Keller. *Instant Neural Graphics Primitives with a Multiresolution Hash Encoding*. ACM Transactions on Graphics, 2022. [Project and publication](https://nvlabs.github.io/instant-ngp/).",
"url": "https://nvlabs.github.io/instant-ngp/",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R14",
"citation": "Tizian Zeltner et al. *Real-Time Neural Appearance Models*. ACM Transactions on Graphics, 2024. [Project and publication](https://research.nvidia.com/labs/rtr/neural_appearance_models/).",
"url": "https://research.nvidia.com/labs/rtr/neural_appearance_models/",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R15",
"citation": "Liwen Wu et al. *8DNA: 8D Neural Asset Light Transport by Distribution Learning*. SIGGRAPH Conference Track, 2026. [Publication](https://research.nvidia.com/labs/rtr/publication/wu20268dna/).",
"url": "https://research.nvidia.com/labs/rtr/publication/wu20268dna/",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R16",
"citation": "Thomas Muller, Fabrice Rousselle, Alexander Keller, Jan Novak. *Neural Control Variates*. ACM Transactions on Graphics, 2020. [Publication](https://research.nvidia.com/publication/2020-11_neural-control-variates).",
"url": "https://research.nvidia.com/publication/2020-11_neural-control-variates",
"checked": "2026-09-19",
"source_type": "primary"
},
{
"id": "R17",
"citation": "Matt Pharr, Wenzel Jakob, Greg Humphreys. *Physically Based Rendering: From Theory to Implementation*, fourth edition, 2023. Monte Carlo Integration, Improving Efficiency. [Book chapter](https://www.pbr-book.org/4ed/Monte_Carlo_Integration/Improving_Efficiency).",
"url": "https://www.pbr-book.org/4ed/Monte_Carlo_Integration/Improving_Efficiency",
"checked": "2026-09-19",
"source_type": "primary"
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] |