HDRGS: High Dynamic Range Gaussian Splatting
<|reference_start|>HDRGS: High Dynamic Range Gaussian Splatting: Recent years have witnessed substantial advancements in the field of 3D reconstruction from 2D images, particularly following the introduction of the neural radiance field (NeRF) technique. However, reconstructing a 3D high dynamic range (HDR) radiance field, which aligns more closely with real-world conditions, from 2D multi-exposure low dynamic range (LDR) images continues to pose significant challenges. Approaches to this issue fall into two categories: grid-based and implicit-based. Implicit methods, using multi-layer perceptrons (MLP), face inefficiencies, limited solvability, and overfitting risks. Conversely, grid-based methods require significant memory and struggle with image quality and long training times. In this paper, we introduce Gaussian Splatting-a recent, high-quality, real-time 3D reconstruction technique-into this domain. We further develop the High Dynamic Range Gaussian Splatting (HDR-GS) method, designed to address the aforementioned challenges. This method enhances color dimensionality by including luminance and uses an asymmetric grid for tone-mapping, swiftly and precisely converting pixel irradiance to color. Our approach improves HDR scene recovery accuracy and integrates a novel coarse-to-fine strategy to speed up model convergence, enhancing robustness against sparse viewpoints and exposure extremes, and preventing local optima. Extensive testing confirms that our method surpasses current state-of-the-art techniques in both synthetic and real-world scenarios. Code will be released at \url{https://github.com/WuJH2001/HDRGS}<|reference_end|>
@article{wu2024hdrgs:,
    title={HDRGS: High Dynamic Range Gaussian Splatting},
    author={Jiahao Wu, Lu Xiao, Rui Peng, Kaiqiang Xiong, Ronggang Wang},
    journal={arXiv preprint arXiv:2408.06543},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06543},
    primaryClass={cs.CV cs.AI}
}