基于流集成的高速体积重建
Racing in Volume with Flow Ensembles
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中文总结 AI 辅助
针对高速运动对象的流式4D重建，提出FastFlowGS方法融合多信号，并构建Monaco4D基准，在效率和精度上显著超越现有方法。
中文摘要 AI 辅助
流式4D重建此前仅在室内环境中得到验证，其依赖于围绕以人类速度移动的对象的密集相机阵列。室外4D重建虽然存在，但要么依赖于安装在移动车辆上的相机，要么依赖于观察准静态对象的有限覆盖阵列，且均为离线处理。对于观众而言，真正重要的场景是快速移动的对象，由稀疏的环境相机环进行流式观察。目前没有方法针对此场景，也没有基准来评估此类方法。为此，我们提出了FastFlowGS，一种流式4D高斯泼溅方法，用于从少量固定外部相机重建快速移动的对象，并提出了Monaco4D，一个基于虚幻引擎5的高保真基准，用于高速室外重建。FastFlowGS通过将稀疏匹配、半稠密轨迹和稠密光流分别提升到3D并赋予几何不确定性，再通过卡尔曼式的时间更新进行融合。Monaco4D提供了在赛道边、车载和无人机视角下、不同光照条件下的F1序列，并带有稠密真值。在CMU-Panoptic上，FastFlowGS以35%更高的效率超越了最强基线12.6%的VMAF。在Monaco4D上，现有流式方法性能严重下降，而FastFlowGS将动态区域PSNR提升了高达18.6%，每帧优化时间降低了28.3%。数据集和更多细节可在该https URL找到。
英文摘要
Streaming 4D reconstruction has been demonstrated only indoors, on dense camera rigs surrounding subjects that move at human pace. Outdoor 4D reconstruction exists but relies either on cameras mounted on the moving vehicle itself, or on limited-coverage arrays observing quasi-static subjects offline. The case that actually matters for spectators is a fast-moving subject, watched from a sparse ring of allocentric cameras, streaming. No method targets this, and no benchmark exists to evaluate one. To this end, we introduce FastFlowGS, a streaming 4D Gaussian Splatting method for reconstructing fast-moving subjects from a small set of fixed external cameras, and Monaco4D, a photorealistic Unreal Engine 5 benchmark for high-speed outdoor reconstruction. FastFlowGS fuses sparse matches, semi-dense tracks, and dense optical flow by lifting each signal to 3D with geometric uncertainty and combining them through a Kalman-style temporal update. Monaco4D provides Formula 1 sequences under varied illumination from trackside, onboard, and drone viewpoints with dense ground truth. On CMU-Panoptic, FastFlowGS exceeds the strongest baseline by 12.6% VMAF at 35% greater efficiency. On Monaco4D, where existing streaming methods degrade severely, it improves dynamic-region PSNR by up to 18.6% with 28.3% lower per-frame optimization time. Dataset and additional details can be found at https://humansensinglab.github.io/monaco4d/.
发表机构
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
