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HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

We propose high dynamic range (HDR) radiance fields, HDR-Plenoxels, that learn a plenoptic function of 3D HDR radiance fields, geometry information, and varying camera settings inherent in 2D low dynamic range (LDR) images. Our voxel-based volume rendering pipeline reconstructs HDR radiance fields with only multi-view LDR images taken from varying camera settings in an end-to-end manner and has a fast convergence speed. To deal with various cameras in real-world scenario, we introduce a tone mapping module that models the digital in-camera imaging pipeline (ISP) and disentangles radiometric settings. Our tone mapping module allows us to render by controlling the radiometric settings of each novel view. Finally, we build a multi-view dataset with varying camera conditions, which fits our problem setting. Our experiments show that HDR-Plenoxels can express detail and high-quality HDR novel views from only LDR images with various cameras.

J.-S. Kim and Y.-J. Kim—Authors contributed equally to this work.

T.-H. Oh—Joint affiliated with Yonsei University, Korea.

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Notes

  1. 1.

    The high-frequency reflectance refers to the case that a subtle view direction change results in drastic reflectance ratio changes, such as glossy materials.

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Acknowledgment

This work was partly supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1C1C1006799), Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00290, Visual Intelligence for Space-Time Understanding and Generation based on Multi-layered Visual Common Sense; and No. 2019-0-01906, Artificial Intelligence Graduate School Program(POSTECH)).

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Jun-Seong, K., Yu-Ji, K., Ye-Bin, M., Oh, TH. (2022). HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields. 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 13692. Springer, Cham. https://doi.org/10.1007/978-3-031-19824-3_23

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  • DOI: https://doi.org/10.1007/978-3-031-19824-3_23

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