A Real-Time Event-Based Normal Flow Estimator
This is an incremental improvement for event-based vision systems, enabling faster processing in applications like robotics or autonomous vehicles.
The paper tackles the problem of real-time normal flow estimation from event cameras by optimizing an existing method to reduce computational cost, achieving speeds of 4-6 million normal flows per second on GPUs.
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.