用随手拍的模糊视频重建高动态范围3D场景，无需固定拍摄位置
Casual3DHDR: Deblurring High Dynamic Range 3D Gaussian Splatting from Casually Captured Videos
- 将连续时间相机轨迹融入物理成像模型，联合优化曝光、位姿和响应函数
- 在真实数据上实现比现有方法更优的渲染质量，容忍严重运动模糊
- 适合移动设备拍摄的非专业视频，突破传统HDR采集限制
从多视角图像实现逼真新视角合成（如NeRF和3D Gaussian Splatting）已获广泛关注。然而，多数方法依赖低动态范围（LDR）图像，在高对比度场景中难以捕捉细节。尽管已有研究关注高动态范围（HDR）场景重建，但通常需在固定相机位置下采集不同曝光时间的清晰图像，耗时且不实用。为提升数据采集灵活性，我们提出 extbf{Casual3DHDR}，一种鲁棒的一阶段方法，可从随意拍摄的自动曝光（AE）视频中重建3D HDR场景，即使存在严重运动模糊和未知变化的曝光时间。该方法将连续时间相机轨迹纳入统一物理成像模型，联合优化曝光时间、相机轨迹与相机响应函数（CRF）。在合成与真实世界数据集上的大量实验表明， extbf{Casual3DHDR}在鲁棒性与渲染质量上均优于现有方法。代码与数据集将公开于https://lingzhezhao.github.io/CasualHDRSplat/
原文摘要 · Abstract (English)
Photo-realistic novel view synthesis from multi-view images, such as neural radiance field (NeRF) and 3D Gaussian Splatting (3DGS), has gained significant attention for its superior performance. However, most existing methods rely on low dynamic range (LDR) images, limiting their ability to capture detailed scenes in high-contrast environments. While some prior works address high dynamic range (HDR) scene reconstruction, they typically require multi-view sharp images with varying exposure times captured at fixed camera positions, which is time-consuming and impractical. To make data acquisition more flexible, we propose \textbf{Casual3DHDR}, a robust one-stage method that reconstructs 3D HDR scenes from casually-captured auto-exposure (AE) videos, even under severe motion blur and unknown, varying exposure times. Our approach integrates a continuous-time camera trajectory into a unified physical imaging model, jointly optimizing exposure times, camera trajectory, and the camera response function (CRF). Extensive experiments on synthetic and real-world datasets demonstrate that \textbf{Casual3DHDR} outperforms existing methods in robustness and rendering quality. Our source code and dataset will be available at https://lingzhezhao.github.io/CasualHDRSplat/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。