Multi-way radial consistency pre-training for event based optical flow

Engineering notes

Validation experiments on Multi Vehicle Stereo Event Camera (MVSEC) dataset demonstrate strong performance: our method achieves 0.67 EPE averaged across all sequences, surpassing E-RAFT (0.89 EPE) and EV-FlowNet (1.10 EPE), without any additional data.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Optical flow estimation is a low-level module in computer vision, widely used in tasks such as visual odometry, autonomous driving, high dynamic range (HDR) imaging, and action recognition. Existing event-based optical flow estimation approaches suffer from scarcity of dense real-world datasets, while some unsupervised frameworks have reduced reliance on large-scale datasets by forward-backward consistency loss, they primarily exploit a 1D temporal reversal, while largely ignoring the rotational and scaling motions ubiquitous in robotics and automotive scenes. This work introduces radial consistency, a self-supervised pre-training framework that maps the event stream to log-polar coordinates and tessellates the spatial domain into K radial rings and L angular sectors, a shared encoder-decoder to predict four complementary flow fields whose cyclic sum is driven to zero, yielding a closed-loop constraint that generalizes the classical forward-backward check to 360° within a sector. Our core contribution , the radial consistency loss, is completely label-free, together with auxiliary terms, enabling self-supervised pre-training on large-scale event data. We optionally apply supervised fine-tuning on small labeled sets to adapt to specific domains, achieving competitive accuracy with fully supervised methods. Validation experiments on Multi Vehicle Stereo Event Camera (MVSEC) dataset demonstrate strong performance: our method achieves 0.67 EPE averaged across all sequences, surpassing E-RAFT (0.89 EPE) and EV-FlowNet (1.10 EPE), without any additional data. On the rotation-heavy indoor_flying3 sequence specifically, we achieve 0.93 EPE (fine-tuned) and 1.49 EPE (self-supervised only) vs. E-RAFT 1.66. We also improve upon E-RAFT in computational efficiency [55 frames per second (FPS) and 26 giga floating-point operations (GFLOPs) vs. 42 FPS and 38 GFLOPs], while requiring only minimal supervised fine-tuning.

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