仅用事件相机实现连续时间3D目标检测，突破高速场景感知瓶颈。
Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection
- 纯事件相机框架，无需同步传统传感器
- 双滤波机制弥补事件数据语义与几何信息缺失
- 适配高速动态场景，适合自动驾驶等实时系统
3D目标检测对自动驾驶系统至关重要，可实现精准定位与尺寸估计。尽管激光雷达和RGB相机广泛应用，其固定帧率在高速场景下仍存在感知盲区。事件相机具有异步特性与高时间分辨率，能连续捕捉运动。现有融合事件相机与常规传感器的连续时间检测方法，在快速运动场景中表现受限，因其依赖传感器同步。本文提出一种全新的立体事件相机3D目标检测框架，完全基于事件相机，无需传统3D传感器。为弥补事件数据缺乏语义与几何信息的问题，引入双滤波机制以提取关键特征。同时通过将边界框与物体中心信息对齐，提升回归精度。实验表明，该方法在动态环境中优于先前方法，验证了事件相机在鲁棒连续时间3D感知中的潜力。代码已公开于https://github.com/mickeykang16/Ev-Stereo3D。
原文摘要 · Abstract (English)
3D object detection is essential for autonomous systems, enabling precise localization and dimension estimation. While LiDAR and RGB cameras are widely used, their fixed frame rates create perception gaps in high-speed scenarios. Event cameras, with their asynchronous nature and high temporal resolution, offer a solution by capturing motion continuously. The recent approach, which integrates event cameras with conventional sensors for continuous-time detection, struggles in fast-motion scenarios due to its dependency on synchronized sensors. We propose a novel stereo 3D object detection framework that relies solely on event cameras, eliminating the need for conventional 3D sensors. To compensate for the lack of semantic and geometric information in event data, we introduce a dual filter mechanism that extracts both. Additionally, we enhance regression by aligning bounding boxes with object-centric information. Experiments show that our method outperforms prior approaches in dynamic environments, demonstrating the potential of event cameras for robust, continuous-time 3D perception. The code is available at https://github.com/mickeykang16/Ev-Stereo3D.
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