Abstract
Radiance of real-world scenes typically spans a much wider dynamic range than what standard cameras can capture. While conventional HDR methods merge alternating-exposure frames, these approaches are inherently constrained to 2D pixel-level alignment, often leading to ghosting artifacts and temporal inconsistency in dynamic scenes. To address these limitations, we present HDR-NSFF, a paradigm shift from 2D-based merging to 4D spatio-temporal modeling. Our framework reconstructs dynamic HDR radiance fields from alternating-exposure monocular videos by representing the scene as a continuous function of space and time, and is compatible with both neural radiance field and 4D Gaussian Splatting (4DGS) based dynamic representations. This unified end-to-end pipeline explicitly models HDR radiance, 3D scene flow, geometry, and tone-mapping, ensuring physical plausibility and global coherence. We further enhance robustness by (i) extending semantic-based optical flow with DINO features to achieve exposure-invariant motion estimation, and (ii) incorporating a generative prior as a regularizer to compensate for limited observation in monocular captures and saturation-induced information loss. To evaluate HDR space-time view synthesis, we present the first real-world HDR-GoPro dataset specifically designed for dynamic HDR scenes. Experiments demonstrate that HDR-NSFF recovers fine radiance details and coherent dynamics even under challenging exposure variations, thereby achieving state-of-the-art performance in novel space-time view synthesis. Project page: this https URL
Abstract (translated)
真实世界的场景亮度范围通常远超出标准相机能够捕捉的范围。虽然传统的高动态范围（HDR）方法通过合并不同曝光度的照片来解决这个问题，但这些方法本质上局限于二维像素级别的对齐，这往往会导致动态场景中的鬼影效应和时间上的一致性问题。为了克服这些限制，我们提出了HDR-NSFF，这是一种从基于2D融合转向4D时空建模的范式转变。 我们的框架通过将场景表示为时间和空间上的连续函数来从交替曝光的单目视频中重建动态HDR辐射场，并且与神经辐射场和4DGaussian Splatting（4DGS）基的动态表示兼容。这一统一的端到端流水线显式地建模了HDR辐射、3D场景流、几何结构及色调映射，确保物理上的可信度和全局一致性。 为了增强鲁棒性，我们通过(i) 使用DINO特征扩展基于语义的光流以实现曝光不变性的运动估计；(ii) 作为正则化器纳入生成先验来弥补单目捕捉中的有限观测以及饱和导致的信息损失。 为评估HDR时空视图合成的效果，我们首次提出了针对动态HDR场景的真实世界HDR-GoPro数据集。实验表明，HDR-NSFF即使在挑战性的曝光变化下也能恢复精细的辐射细节和连贯的动力学，并因此达到了新型时空视图合成中的最先进性能。 项目页面：[此URL](this%20https%20URL)
URL
https://arxiv.org/abs/2603.08313
