Neural Shadow Mapping
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Abstract (arXiv)
We present a neural extension of basic shadow mapping for fast, high quality hard and soft shadows. We compare favorably to fast pre-filtering shadow mapping, all while producing visual results on par with ray traced hard and soft shadows. We show that combining memory bandwidth-aware architecture specialization and careful temporal-window training leads to a fast, compact and easy-to-train neural shadowing method. Our technique is memory bandwidth conscious, eliminates the need for post-process temporal anti-aliasing or denoising, and supports scenes with dynamic view, emitters and geometry while remaining robust to unseen objects.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jan 2023.
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Citing papers per year: 2023: 3; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2023Real-Time Neural Materials using Block-Compressed Featurescited by 4
- 2023Deep and Fast Approximate Order Independent Transparencycited by 3
- 2023Efficient Graphics Representation with Differentiable Indirectioncited by 2
- 2026STAR-NT: Spatiotemporal Acceleration of Real-Time Neural Transparency Renderingcited by 0
- 2026STAR-NT: Spatiotemporal Acceleration of Real-Time Neural Transparency Rendering15 Jun 2026
- 2023Real-Time Neural Materials using Block-Compressed Features26 Oct 2023
- 2023Efficient Graphics Representation with Differentiable Indirection12 Sep 2023
- 2023Deep and Fast Approximate Order Independent Transparency17 May 2023