AI 中文总结
针对SPAD流密集光流计算的依赖问题，提出首个直接从SPAD流计算密集光流的方法QuantaFlow，通过迭代优化实现光流估计，在合成与真实SPAD数据上验证了其有效性和泛化性。
AI 中文摘要
光流在高速和低光照场景中仍具挑战性，传统相机有限的帧率和灵敏度会导致运动模糊和曝光不足。单光子雪崩二极管（SPAD）相机具备单光子灵敏度和极高的时间采样率，但这些高帧率二进制光子流中的单个切片过于稀疏，无法实现密集对应。时间聚合可提供光流所需的空间线索，但在固定坐标下累积光子会使运动结构模糊；感知运动的聚合虽能减少这种模糊，却依赖于待估计的光流。为解决这种依赖问题，我们提出QuantaFlow，这是首个直接从SPAD流计算密集光流的方法。QuantaFlow不构建固定输入表示，而是将SPAD表示构建嵌入迭代光流优化过程：每次迭代中，当前光流粗略对齐源和目标子流内的切片，随后通过光子通量变换构建包含强度和结构线索的多尺度表示，同时自适应多尺度融合在每个像素处平衡光子噪声和残留运动模糊；融合后的表示驱动特征扭曲光流更新，优化后的光流则指导下一次迭代的表示构建。我们还构建了用于SPAD光流训练和评估的合成数据集，在合成数据集和真实SPAD数据上的实验验证了QuantaFlow的有效性和泛化性。
英文摘要
Optical flow remains challenging in high-speed and low-light scenes, where the limited frame rate and sensitivity of conventional cameras lead to motion blur and underexposure. Single-photon avalanche diode (SPAD) cameras offer single-photon sensitivity and extremely fine temporal sampling. However, individual slices in these high FPS binary photon streams are too sparse for dense correspondence. Temporal aggregation can provide the spatial cues required by optical flow, but accumulating photons at fixed coordinates blurs moving structures. Motion-aware aggregation can reduce this blur, yet it depends on the flow being estimated. To address this dependency, we propose QuantaFlow, the first method for dense optical flow directly from SPAD streams. Instead of constructing a fixed input representation, QuantaFlow embeds SPAD representation construction into iterative flow refinement. At each iteration, the current flow coarsely aligns the slices within the source and target sub-streams. A photon-flux transformation then constructs multi-scale representations containing intensity and structural cues, while adaptive multi-scale fusion balances photon noise and residual motion blur at each pixel. The fused representations drive a feature-warping flow update, and the refined flow guides representation construction in the next iteration. We further construct a synthetic dataset for SPAD optical-flow training and evaluation. Experiments on the synthetic dataset and real-world SPAD data demonstrate the effectiveness and generalization of QuantaFlow.
