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.
近年来，随着神经辐射场（NeRF）技术的引入，基于2D图像的3D重建领域取得了显著进展。然而，从2D多曝光低动态范围（LDR）图像重建与现实条件更为接近的3D高动态范围（HDR）辐射场仍然面临重大挑战。针对这一问题的方法通常分为两类：基于网格的方法和基于隐式的方法。隐式方法通常使用多层感知机（MLP），但存在效率低下、可解性有限以及过拟合的风险。相比之下，基于网格的方法虽然内存需求巨大，但在图像质量和训练时间方面仍存在困难。 在本文中，我们将高质量、实时的3D重建技术——高斯点绘（Gaussian Splatting）引入到这一领域，并进一步开发了高动态范围高斯点绘（HDR-GS）方法，旨在解决上述挑战。该方法通过引入亮度来增强颜色维度，并使用非对称网格进行色调映射，从而快速且准确地将像素辐照度转换为颜色。我们的方法不仅提高了HDR场景恢复的准确性，还集成了一种粗到细的策略，加速了模型的收敛，增强了在稀疏视角和极端曝光下的鲁棒性，并避免了局部最优解。广泛的测试结果表明，我们的方法在合成和现实场景中均超越了当前的最先进技术。