References & Background
Kiln is an implementation-focused project that builds on well-established ideas in volume rendering, sparse streaming, and real-time graphics. It adapts proven techniques to a modern WebGPU context.
Academic & technical references
The following works were particularly influential during development:
Barrett, S. (2008). Sparse Virtual Textures. Game Developers Conference (GDC) 2008. http://silverspaceship.com/src/svt/
CesiumGS. (2019). 3D Tiles: Specification for Streaming Massive Heterogeneous 3D Geospatial Datasets. Open Geospatial Consortium (OGC) Community Standard. https://github.com/CesiumGS/3d-tiles
Engel, K., Hadwiger, M., Kniss, J., Rezk-Salama, C., & Weiskopf, D. (2006). Real-Time Volume Graphics. A K Peters/CRC Press. https://doi.org/10.1201/b10629
Karis, B. (2014). High Quality Temporal Supersampling. ACM SIGGRAPH 2014, Advances in Real-Time Rendering in Games. https://de45xmedrsdbp.cloudfront.net/Resources/files/TemporalAA_small-59732822.pdf
Levoy, M. (1990). Efficient ray tracing of volume data. ACM Transactions on Graphics, 9(3), 245–261. https://doi.org/10.1145/78964.78965
Lux, C., & Fröhlich, B. (2009). GPU-Based Ray Casting of Multiple Multi-resolution Volume Datasets. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. https://link.springer.com/chapter/10.1007/978-3-642-10520-3_10
Maitin-Shepard, J., et al. (2021). Neuroglancer: Web-based volumetric data visualization. https://github.com/google/neuroglancer
Moore, J., et al. (2023). OME-Zarr: a cloud-optimized bioimaging file format with international community support. Histochemistry and Cell Biology, 160(3), 223–251. https://doi.org/10.1007/s00418-023-02209-1
Schütz, M. (2016). Potree: Rendering Large Point Clouds in Web Browsers [Master's thesis, Technische Universität Wien]. https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/
W3C GPU for the Web Working Group. (2026). WebGPU. W3C Candidate Recommendation Draft. https://gpuweb.github.io/gpuweb/
IDR. (2024). OME-NGFF Samples Directory. Image Data Resource. https://idr.github.io/ome-ngff-samples/
Dataset credits
Dataset attributions live alongside the demos in the Gallery.