The NEOLIX Open Dataset for Autonomous Driving

TL;DR

The NEOLIX dataset provides a large-scale, diverse collection of annotated data to support the development and evaluation of perception models in autonomous driving, covering various conditions and regions.

Contribution

This paper introduces the NEOLIX dataset with extensive annotations and tools, filling a gap in large-scale datasets for autonomous vehicle perception research.

Findings

Contains 30,000 frames with point cloud labels

Includes over 600,000 3D bounding boxes with annotations

Covers diverse regions and driving conditions

Abstract

With the gradual maturity of 5G technology,autonomous driving technology has attracted moreand more attention among the research commu-nity. Autonomous driving vehicles rely on the co-operation of artificial intelligence, visual comput-ing, radar, monitoring equipment and GPS, whichenables computers to operate motor vehicles auto-matically and safely without human interference.However, the large-scale dataset for training andsystem evaluation is still a hot potato in the devel-opment of robust perception models. In this paper,we present the NEOLIX dataset and its applica-tions in the autonomous driving area. Our datasetincludes about 30,000 frames with point cloud la-bels, and more than 600k 3D bounding boxes withannotations. The data collection covers multipleregions, and various driving conditions, includingday, night, dawn, dusk and sunny day. In orderto label this complete dataset, we developed vari-ous tools and algorithms specified for each task tospeed up the labelling process. It is expected thatour dataset and related algorithms can support andmotivate researchers for the further developmentof autonomous driving in the field of computer vi-sion.