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L-Tracing: Fast Light Visibility Estimation on Neural Surfaces by Sphere Tracing

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

We introduce a highly efficient light visibility estimation method, called L-Tracing, for reflectance factorization on neural implicit surfaces. Light visibility is indispensable for modeling shadows and specular of high quality on object’s surface. For neural implicit representations, former methods of computing light visibility suffer from efficiency and quality drawbacks. L-Tracing leverages the distance meaning of the Signed Distance Function(SDF), and computes the light visibility of the solid object surface according to binary geometry occlusions. We prove the linear convergence of L-Tracing algorithm and give out the theoretical lower bound of tracing iteration. Based on L-Tracing, we propose a new surface reconstruction and reflectance factorization framework. Experiments show our framework performs nearly speedup on factorization, and achieves competitive albedo and relighting results with existing approaches.

The work is done when Ziyu Chen is an intern at SenseTime.

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Acknowledgement

The authors Ziyu Chen and Li Song were supported by the Shanghai Key Laboratory of Digital Media Processing and Transmissions.

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Correspondence to Li Song .

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Chen, Z. et al. (2022). L-Tracing: Fast Light Visibility Estimation on Neural Surfaces by Sphere Tracing. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13675. Springer, Cham. https://doi.org/10.1007/978-3-031-19784-0_13

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  • DOI: https://doi.org/10.1007/978-3-031-19784-0_13

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