KiloNeuS: Implicit Neural Representations with Real-Time Global
Illumination
- URL: http://arxiv.org/abs/2206.10885v1
- Date: Wed, 22 Jun 2022 07:33:26 GMT
- Title: KiloNeuS: Implicit Neural Representations with Real-Time Global
Illumination
- Authors: Stefano Esposito, Daniele Baieri, Stefan Zellmann, Andr\'e Hinkenjann,
Emanuele Rodol\`a
- Abstract summary: We present KiloNeuS, a new neural object representation that can be rendered in path-traced scenes at interactive frame rates.
KiloNeuS enables the simulation of realistic light interactions between neural and classic primitives in shared scenes.
- Score: 1.5749416770494706
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The latest trends in inverse rendering techniques for reconstruction use
neural networks to learn 3D representations as neural fields. NeRF-based
techniques fit multi-layer perceptrons (MLPs) to a set of training images to
estimate a radiance field which can then be rendered from any virtual camera by
means of volume rendering algorithms. Major drawbacks of these representations
are the lack of well-defined surfaces and non-interactive rendering times, as
wide and deep MLPs must be queried millions of times per single frame. These
limitations have recently been singularly overcome, but managing to accomplish
this simultaneously opens up new use cases. We present KiloNeuS, a new neural
object representation that can be rendered in path-traced scenes at interactive
frame rates. KiloNeuS enables the simulation of realistic light interactions
between neural and classic primitives in shared scenes, and it demonstrably
performs in real-time with plenty of room for future optimizations and
extensions.
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