用事件相机模拟生物视觉，局部对齐事件估算物体远近。
Active Event Alignment for Monocular Distance Estimation
- 基于生物视觉机制，通过局部事件对齐估计距离。
- 在EVIMO2上提升16%精度，优于现有方法。
- 适合需要低延迟距离感知的机器人与自动驾驶场景。
事件相机提供了自然且高效的数据表示，激发了新型视觉信息提取策略。受生物视觉系统启发，我们提出一种行为驱动的方法，实现基于事件相机数据的物体级距离估计。该方法模拟人类眼睛等生物系统依据物体距离调整视图稳定性的机制：远处物体只需微小补偿旋转即可保持聚焦，而近处物体则需更大调整以维持对齐。此自适应策略利用自然稳定的运动规律，有效估计相对距离。不同于传统算法在全图范围估计深度，本方法聚焦特定感兴趣区域内的局部深度估计。通过对小区域内事件进行对齐，估算出使图像运动稳定的角速度。我们证明，在特定假设下，补偿性旋转流与物体距离成反比。所提方法在复杂相机运动和显著深度变化的真实场景数据集EVIMO2上达到新的最先进性能，精度提升16%。
原文摘要 · Abstract (English)
Event cameras provide a natural and data efficient representation of visual information, motivating novel computational strategies towards extracting visual information. Inspired by the biological vision system, we propose a behavior driven approach for object-wise distance estimation from event camera data. This behavior-driven method mimics how biological systems, like the human eye, stabilize their view based on object distance: distant objects require minimal compensatory rotation to stay in focus, while nearby objects demand greater adjustments to maintain alignment. This adaptive strategy leverages natural stabilization behaviors to estimate relative distances effectively. Unlike traditional vision algorithms that estimate depth across the entire image, our approach targets local depth estimation within a specific region of interest. By aligning events within a small region, we estimate the angular velocity required to stabilize the image motion. We demonstrate that, under certain assumptions, the compensatory rotational flow is inversely proportional to the object's distance. The proposed approach achieves new state-of-the-art accuracy in distance estimation - a performance gain of 16% on EVIMO2. EVIMO2 event sequences comprise complex camera motion and substantial variance in depth of static real world scenes.
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