Mono4DGS-HDR: 从交替曝光单目视频中重建可渲染的4D高动态范围场景
Mono4DGS-HDR: High Dynamic Range 4D Gaussian Splatting from Alternating-exposure Monocular Videos
AI总结：
Mono4DGS-HDR通过两阶段优化方法，从交替曝光单目视频中高效重建可渲染的4D HDR场景。
摘要：
我们介绍Mono4DGS-HDR，这是首个能够从未对准的单目低动态范围视频中重建可渲染的4D高动态范围（HDR）场景的系统。为解决这一具有挑战性的问题，我们提出了一种基于高斯点划的统一框架，采用两阶段优化方法。第一阶段在正交相机坐标空间中学习视频HDR高斯表示，消除了对相机姿态的依赖，使初始HDR视频重建更加稳健。第二阶段将视频高斯转换到世界空间，并联合优化世界空间的高斯与相机姿态。此外，我们提出了一种时间亮度正则化策略，以增强HDR外观的时间一致性。由于我们的任务此前未被研究，我们使用公开可用的数据集构建了一个新的评估基准。大量实验表明，Mono4DGS-HDR在渲染质量和速度上均显著优于基于最新方法的替代方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
