HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields
This addresses the challenge of creating HDR 3D scenes from real-world LDR images for applications in computer vision and graphics.
The paper tackles the problem of reconstructing high dynamic range (HDR) radiance fields from multi-view low dynamic range (LDR) images taken with varying camera settings, achieving detail and high-quality HDR novel views.
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 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.