Cirrus: A Long-range Bi-pattern LiDAR Dataset

TL;DR

Cirrus is a new long-range LiDAR dataset with diverse scanning patterns and comprehensive annotations, designed to advance autonomous driving research in highway scenarios.

Contribution

It provides a high-resolution, long-range LiDAR dataset with multiple scanning patterns and exhaustive annotations, enabling new research on LiDAR model adaptation.

Findings

Demonstrates the dataset's utility for LiDAR model adaptation.

Shows the impact of scanning patterns on object detection.

Highlights the dataset's potential for autonomous driving research.

Abstract

In this paper, we introduce Cirrus, a new long-range bi-pattern LiDAR public dataset for autonomous driving tasks such as 3D object detection, critical to highway driving and timely decision making. Our platform is equipped with a high-resolution video camera and a pair of LiDAR sensors with a 250-meter effective range, which is significantly longer than existing public datasets. We record paired point clouds simultaneously using both Gaussian and uniform scanning patterns. Point density varies significantly across such a long range, and different scanning patterns further diversify object representation in LiDAR. In Cirrus, eight categories of objects are exhaustively annotated in the LiDAR point clouds for the entire effective range. To illustrate the kind of studies supported by this new dataset, we introduce LiDAR model adaptation across different ranges, scanning patterns, and sensor devices. Promising results show the great potential of this new dataset to the robotics and computer vision communities.