PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving

TL;DR

PandaSet is a comprehensive, high-quality autonomous driving dataset featuring multi-sensor data, extensive annotations, and baseline models, aimed at advancing perception algorithms in self-driving vehicles.

Contribution

This paper introduces PandaSet, a new large-scale autonomous vehicle dataset with diverse sensors, detailed annotations, and baseline benchmarks, created with a complete sensor suite and no-cost license.

Findings

Provides over 100 annotated scenes for autonomous driving research

Includes baseline models for LiDAR-only and sensor fusion detection

Offers extensive labels for object classification and segmentation

Abstract

The accelerating development of autonomous driving technology has placed greater demands on obtaining large amounts of high-quality data. Representative, labeled, real world data serves as the fuel for training deep learning networks, critical for improving self-driving perception algorithms. In this paper, we introduce PandaSet, the first dataset produced by a complete, high-precision autonomous vehicle sensor kit with a no-cost commercial license. The dataset was collected using one 360{\deg} mechanical spinning LiDAR, one forward-facing, long-range LiDAR, and 6 cameras. The dataset contains more than 100 scenes, each of which is 8 seconds long, and provides 28 types of labels for object classification and 37 types of labels for semantic segmentation. We provide baselines for LiDAR-only 3D object detection, LiDAR-camera fusion 3D object detection and LiDAR point cloud segmentation. For more details about PandaSet and the development kit, see https://scale.com/open-datasets/pandaset.