构建合成事件流数据集eCARLA-scenes，助力神经形态视觉导航
eCARLA-scenes: A synthetically generated dataset for event-based optical flow prediction
- 基于CARLA仿真器生成多样驾驶场景事件流数据
- 提供完整数据处理工具链与训练评估支持
- 适合研究事件相机与脉冲神经网络的开发者
事件视觉与脉冲神经网络（SNNs）在机器人领域前景广阔，可用于视觉里程计和避障等任务。尽管已有真实事件数据集（多由无人机采集），但其多样性、可扩展性受限且难以获取。为此，本文提出eWiz工具库，涵盖事件数据加载、增强、可视化、编码及训练数据生成等功能，并配套损失函数与评估指标。在此基础上，构建了基于CARLA模拟器的合成事件数据集eCARLA-scenes，用于光学流预测任务。该数据集旨在覆盖多样化环境，为自主车辆导航中的事件相机应用奠定基础，推动SNN在英特尔Loihi类神经形态硬件上的部署。
原文摘要 · Abstract (English)
The joint use of event-based vision and Spiking Neural Networks (SNNs) is expected to have a large impact in robotics in the near future, in tasks such as, visual odometry and obstacle avoidance. While researchers have used real-world event datasets for optical flow prediction (mostly captured with Unmanned Aerial Vehicles (UAVs)), these datasets are limited in diversity, scalability, and are challenging to collect. Thus, synthetic datasets offer a scalable alternative by bridging the gap between reality and simulation. In this work, we address the lack of datasets by introducing eWiz, a comprehensive library for processing event-based data. It includes tools for data loading, augmentation, visualization, encoding, and generation of training data, along with loss functions and performance metrics. We further present a synthetic event-based datasets and data generation pipelines for optical flow prediction tasks. Built on top of eWiz, eCARLA-scenes makes use of the CARLA simulator to simulate self-driving car scenarios. The ultimate goal of this dataset is the depiction of diverse environments while laying a foundation for advancing event-based camera applications in autonomous field vehicle navigation, paving the way for using SNNs on neuromorphic hardware such as the Intel Loihi.
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