从单目交替曝光视频重建高动态范围4D场景，无需相机位姿。
Mono4DGS-HDR: High Dynamic Range 4D Gaussian Splatting from Alternating-exposure Monocular Videos
- 分两阶段优化：先在正交坐标系建模HDR视频，再转至世界空间联合优化
- 在多个数据集上渲染质量与速度均显著优于现有方法
- 适用于无位姿约束的高动态范围视频重建任务
我们提出Mono4DGS-HDR，首个从无位姿单目低动态范围（LDR）交替曝光视频中重建可渲染4D高动态范围（HDR）场景的系统。为解决这一挑战，我们设计了基于高斯点阵的统一两阶段优化框架。第一阶段在正交相机坐标系中学习视频级HDR高斯表示，无需相机位姿即可实现鲁棒的初始HDR视频重建。第二阶段将视频高斯转换至世界空间，并联合优化世界空间高斯与相机位姿。此外，提出时间亮度正则化策略，提升HDR外观的时间一致性。由于该任务此前未被研究，我们基于公开数据集构建了新的评估基准。大量实验表明，Mono4DGS-HDR在渲染质量和速度上均显著优于基于现有先进方法适配的替代方案。
原文摘要 · Abstract (English)
We introduce Mono4DGS-HDR, the first system for reconstructing renderable 4D high dynamic range (HDR) scenes from unposed monocular low dynamic range (LDR) videos captured with alternating exposures. To tackle such a challenging problem, we present a unified framework with two-stage optimization approach based on Gaussian Splatting. The first stage learns a video HDR Gaussian representation in orthographic camera coordinate space, eliminating the need for camera poses and enabling robust initial HDR video reconstruction. The second stage transforms video Gaussians into world space and jointly refines the world Gaussians with camera poses. Furthermore, we propose a temporal luminance regularization strategy to enhance the temporal consistency of the HDR appearance. Since our task has not been studied before, we construct a new evaluation benchmark using publicly available datasets for HDR video reconstruction. Extensive experiments demonstrate that Mono4DGS-HDR significantly outperforms alternative solutions adapted from state-of-the-art methods in both rendering quality and speed.
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