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arXiv · 2412.09209

eCARLA-scenes: A synthetically generated dataset for event-based optical flow prediction

Jad Mansour·Hayat Rajani·Rafael Garcia·Nuno Gracias
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Abstract

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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Jad Mansour, Hayat Rajani, Rafael Garcia, Nuno Gracias. 2024-12-12. eCARLA-scenes: A synthetically generated dataset for event-based optical flow prediction. https://arxiv.org/abs/2412.09209

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