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KiloNeuS: Implicit Neural Representations with Real-Time Global Illumination

  • 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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Document Type:Preprint
Author:Stefano Esposito, Daniele Baieri, Stefan Zellmann, André Hinkenjann, Emanuele Rodolà
Number of pages:8
ArXiv Id:http://arxiv.org/abs/2206.10885
Date of first publication:2022/06/22
Funding:This work is partially supported by the ERC grant no 802554 (SPEC-GEO).
Keyword:Neural representations; path tracing; real-time
Departments, institutes and facilities:Fachbereich Wirtschaftswissenschaften
Institute of Visual Computing (IVC)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
Entry in this database:2022/07/12