配向距離場によるイベントベースのモーション推定
Event-Based Motion Estimation via Oriented Distance Fields
イベントカメラの動き推定を、反復最適化の代わりに事前計算した距離場の平均化で行う手法を提案し、低遅延かつ高精度を実現。さらに、ぼけ除去と瞳孔追跡への応用も示した。
著者: Lei Sun, Yuqin Ma, Weilun Li, Haoran Liang, Runyi Yang, Kaiwei Wang, Danda Pani Paudel, Luc Van Gool
分類: cs.CV, cs.RO
原文アブストラクト
Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion. Existing methods typically rely on iterative optimization or repeated hypothesis comparison, offsetting the sensor's low-latency advantage. We propose Oriented Distance Field Motion Estimation (ODF Motion Estimation), which replaces this optimization with a single averaging step over a precomputed field of event distance vectors, combined with an adaptive event-count selection strategy and a parameter-free trail filter. On public and self-collected datasets, ODF motion estimation reaches sub-pixel accuracy at the lowest latency among compared methods. We validate its generality on two downstream applications rather than treating them as separate contributions. First, the estimated trajectory is converted into a blur kernel and paired with a compact iterative-unfolding network, trained on simulated motion-estimation noise, for real-time non-blind image deblurring, attaining competitive or superior PSNR/SSIM with under 1M parameters. Second, the same precomputed field is repurposed for directional event filtering in a low-power asynchronous pupil and glint tracker, sustaining stable tracking for tens of seconds while lowering a near-eye module's power draw.