Publikation
N-BVH: Neural ray queries with bounding volume hierarchies
Philippe Weier; Alexander Rath; Élie Michel; Iliyan Georgiev; Philipp Slusallek; Tamy Boubekeur
In: Andres Burbano; Denis Zorin; Wojciech Jarosz (Hrsg.). Proceedings - SIGGRAPH 2024 Conference Papers. ACM Siggraph (Siggraph-24), July 27 - August 1, Denver, CO, USA, Pages 1-11, Vol. 25, ISBN 979-8-4007-0525-0, Association for Computing Machinery, New York, NY, USA, 7/2024.
Zusammenfassung
Neural representations have shown spectacular ability to compress
complex signals in a fraction of the raw data size. In 3D computer
graphics, the bulk of a scene’s memory usage is due to polygons
and textures, making them ideal candidates for neural compression.
Here, the main challenge lies in finding good trade-offs between efficient compression and cheap inference while minimizing training
time. In the context of rendering, we adopt a ray-centric approach to
this problem and devise N-BVH, a neural compression architecture
designed to answer arbitrary ray queries in 3D. Our compact mode is learned from the input geometry and substituted for it whenever
a ray intersection is queried by a path-tracing engine. While prior
neural compression methods have focused on point queries, ours
proposes neural ray queries that integrate seamlessly into standard
ray-tracing pipelines. At the core of our method, we employ an
adaptive BVH-driven probing scheme to optimize the parameters
of a multi-resolution hash grid, focusing its neural capacity on the
sparse 3D occupancy swept by the original surfaces. As a result, our
N-BVH can serve accurate ray queries from a representation that is
more than an order of magnitude more compact, providing faithful
approximations of visibility, depth, and appearance attributes. The
flexibility of our method allows us to combine and overlap neural
and non-neural entities within the same 3D scene and extends to
appearance level of detail.