实时计算事件相机的法向流，速度超600万帧/秒。
A Real-Time Event-Based Normal Flow Estimator
- 将事件坐标整数特性转化为池化操作，降低计算复杂度。
- 在RTX 3070上达400万法向流/秒，在RTX A5000上达600万/秒。
- 开源代码支持实时应用，适合事件相机系统开发者。
本文提出一种实时、异步、基于事件的法向流估计算法。该方法沿用《直接从事件邻域学习法向流》的算法框架，但实现更高效：原方法将事件切片视为三维点云，通过多层感知机预测法向流，利用邻接矩阵与特征矩阵相乘构建表示，时间复杂度为事件数的平方。本文利用事件坐标的整数特性，将表示步骤重构成池化操作，达到相同效果且计算开销大幅降低。最终实现事件相机上的实时法向流预测。该估算器占用1GB CUDA内存，在RTX 3070上运行速度达每秒400万法向流，在RTX A5000上达每秒600万。代码已开源，提供CUDA及Python接口。
原文摘要 · Abstract (English)
This paper presents a real-time, asynchronous, event-based normal flow estimator. It follows the same algorithm as Learning Normal Flow Directly From Event Neighborhoods, but with a more optimized implementation. The original method treats event slices as 3D point clouds, encodes each event's local geometry into a fixed-length vector, and uses a multi-layer perceptron to predict normal flow. It constructs representations by multiplying an adjacency matrix with a feature matrix, resulting in quadratic time complexity with respect to the number of events. In contrast, we leverage the fact that event coordinates are integers and reformulate the representation step as a pooling operation. This achieves the same effect as the adjacency matrix but with much lower computational cost. As a result, our method supports real-time normal flow prediction on event cameras. Our estimator uses 1 GB of CUDA memory and runs at 4 million normal flows per second on an RTX 3070, or 6 million per second on an RTX A5000. We release the CUDA implementation along with a Python interface at https://github.com/dhyuan99/VecKM_flow_cpp.
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