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

POSTECH
European Conference on Computer Vision (ECCV) 2022

1) Plenoxels synthesize an HDR image from HDR radiance by ray-marching, then 2) the differentiable tone-mapping function maps from HDR to LDR in an end-to-end manner.

Abstract

We propose high dynamic range radiance (HDR) 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 scenarios, 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.

Video

BibTeX


    @inproceedings{jun2022hdr,
        title     = {HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields},
        author    = {Jun-Seong, Kim and Yu-Ji, Kim and Ye-Bin, Moon and Oh, Tae-Hyun},
        booktitle = {ECCV},
        year      = {2022},
    }