The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception

TL;DR

This paper introduces a comprehensive, multi-environment dataset for stereo event-based cameras, including synchronized event streams, images, IMU, and ground truth data to facilitate 3D perception research.

Contribution

It provides a large, diverse dataset with synchronized stereo event cameras, IMU, lidar, motion capture, and GPS data, filling a critical gap for developing and testing 3D perception algorithms.

Findings

Dataset covers various environments and illumination conditions.

Includes synchronized event streams, images, IMU, lidar, and pose data.

Enables benchmarking and development of 3D perception algorithms.

Abstract

Event based cameras are a new passive sensing modality with a number of benefits over traditional cameras, including extremely low latency, asynchronous data acquisition, high dynamic range and very low power consumption. There has been a lot of recent interest and development in applying algorithms to use the events to perform a variety of 3D perception tasks, such as feature tracking, visual odometry, and stereo depth estimation. However, there currently lacks the wealth of labeled data that exists for traditional cameras to be used for both testing and development. In this paper, we present a large dataset with a synchronized stereo pair event based camera system, carried on a handheld rig, flown by a hexacopter, driven on top of a car and mounted on a motorcycle, in a variety of different illumination levels and environments. From each camera, we provide the event stream, grayscale images and IMU readings. In addition, we utilize a combination of IMU, a rigidly mounted lidar system, indoor and outdoor motion capture and GPS to provide accurate pose and depth images for each camera at up to 100Hz. For comparison, we also provide synchronized grayscale images and IMU readings from a frame based stereo camera system.