1 · Introduction

An accurate High Definition (HD) Maps with lane markings usually serves as the back-end for all commercial auto-drive vehicles for navigation. Currently, most HD maps are constructed manually by human labelers. In this challenge, we require participants to develop algorithms to extract all basic road elements from RGB image frames. The segmentation results can be directly used for HD Maps construction or updating process.

This repository contains the evaluation scripts for the landmark detection challenge of the ApolloScapes dataset. This large-scale dataset contains a diverse set of stereo video sequences recorded in street scenes from different cities, with high quality pixel-level annotations of 110 000+ frames.

2 · Dataset Download

Sample data

Training data

Testing data

More data structure information of our dataset, the evaluation metric and script can be found via our github website.
If you want to participate in our challenge, please submit your results here.

3 · Leaderboard

Rank
Method
Mean Iou
Time
Team Name

4 · Publication

Please cite our paper in your publications if our dataset is used in your research.
Xinyu Huang, Xinjing Cheng, Qichuan Geng, Binbin Cao, Dingfu Zhou, Peng Wang, Yuanqing Lin, and Ruigang Yang, The ApolloScape Dataset for Autonomous Driving, arXiv: 1803.06184, 2018
[PDF]   [BibTex]

The dataset we released is  desensitized street view  for academic use only.