Racing in Volume with Flow Ensembles

Streaming 4D reconstruction has been demonstrated onlyindoors, on dense camera rigs surrounding subjects that move at hu-man pace. Outdoor 4D reconstruction exists but relies either on camerasmounted on the moving vehicle itself, or on limited-coverage arrays ob-serving quasi-static subjects offline. The case that actually matters forspectators is a fast-moving subject, watched from a sparse ring of allocen-tric cameras, streaming. No method targets this, and no benchmark ex-ists to evaluate one. To this end, we introduce FastFlowGS, a streaming4D Gaussian Splatting method for reconstructing fast-moving subjectsfrom a small set of fixed external cameras, and Monaco4D, a photoreal-istic Unreal Engine 5 benchmark for high-speed outdoor reconstruction.FastFlowGS fuses sparse matches, semi-dense tracks, and dense opticalflow by lifting each signal to 3D with geometric uncertainty and combin-ing them through a Kalman-style temporal update. Monaco4D providesFormula 1 sequences under varied illumination from trackside, onboard,and drone viewpoints with dense ground truth. On CMU-Panoptic (apublic dataset), FastFlowGS exceeds the strongest baseline by 12.6%VMAF at 35% greater efficiency. On Monaco4D, where existing stream-ing methods degrade severely, it improves dynamic-region PSNR by upto 18.6% with 28.3% lower per-frame optimization time.