3D-FlowNet: Event-based optical flow estimation with 3D representation

TL;DR

This paper introduces 3D-FlowNet, a novel neural network architecture that processes 3D event data from event cameras to estimate optical flow, improving temporal resolution preservation and outperforming existing methods.

Contribution

The paper proposes a new 3D encoding for event data and a neural network architecture, 3D-FlowNet, trained with self-supervision, advancing event-based optical flow estimation.

Findings

3D encoding better preserves temporal information.

3D-FlowNet outperforms state-of-the-art methods.

Requires fewer training epochs (30 vs. 100).

Abstract

Event-based cameras can overpass frame-based cameras limitations for important tasks such as high-speed motion detection during self-driving cars navigation in low illumination conditions. The event cameras' high temporal resolution and high dynamic range, allow them to work in fast motion and extreme light scenarios. However, conventional computer vision methods, such as Deep Neural Networks, are not well adapted to work with event data as they are asynchronous and discrete. Moreover, the traditional 2D-encoding representation methods for event data, sacrifice the time resolution. In this paper, we first improve the 2D-encoding representation by expanding it into three dimensions to better preserve the temporal distribution of the events. We then propose 3D-FlowNet, a novel network architecture that can process the 3D input representation and output optical flow estimations according to the new encoding methods. A self-supervised training strategy is adopted to compensate the lack of labeled datasets for the event-based camera. Finally, the proposed network is trained and evaluated with the Multi-Vehicle Stereo Event Camera (MVSEC) dataset. The results show that our 3D-FlowNet outperforms state-of-the-art approaches with less training epoch (30 compared to 100 of Spike-FlowNet).