HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields
원문 초록
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.
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Towards Realistic Low-Light Image Enhancement via ISP Driven Data Modeling
GS-I$^{3}$: Gaussian Splatting for Surface Reconstruction from Illumination-Inconsistent Images
Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images
Deep chroma compression of tone-mapped images
HDRSplat: Gaussian Splatting for High Dynamic Range 3D Scene Reconstruction from Raw Images
Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesis
토론
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