bibliography.bib

@article{coomans2024realtime,
    author = {Coomans, Arno and Dominci, Edoardo A. and Döring, Christian and
              Mueller, Joerg H. and Hladky, Jozef and Steinberger, Markus},
    title = {Real-time Neural Rendering of Dynamic Light Fields},
    journal = {Computer Graphics Forum},
    volume = {n/a},
    number = {n/a},
    pages = {e15014},
    keywords = {CCS Concepts, • Computing methodologies → Ray tracing, Neural
                networks},
    doi = {https://doi.org/10.1111/cgf.15014},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.15014},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.15014},
    abstract = {Abstract Synthesising high-quality views of dynamic scenes via
                path tracing is prohibitively expensive. Although caching
                offline-quality global illumination in neural networks alleviates
                this issue, existing neural view synthesis methods are limited to
                mainly static scenes, have low inference performance or do not
                integrate well with existing rendering paradigms. We propose a
                novel neural method that is able to capture a dynamic light field
                , renders at real-time frame rates at 1920× resolution and
                integrates seamlessly with Monte Carlo ray tracing frameworks. We
                demonstrate how a combination of spatial, temporal and a novel
                surface-space encoding are each effective at capturing different
                kinds of spatio-temporal signals. Together with a compact
                fully-fused neural network and architectural improvements, we
                achieve a twenty-fold increase in network inference speed
                compared to related methods at equal or better quality. Our
                approach is suitable for providing offline-quality real-time
                rendering in a variety of scenarios, such as free-viewpoint video
                , interactive multi-view rendering, or streaming rendering.
                Finally, our work can be integrated into other rendering
                paradigms, e.g., providing a dynamic background for interactive
                scenarios where the foreground is rendered with traditional
                methods.},
}