# How to Build a Curb Dataset with LiDAR Data for Autonomous Driving

arXiv: https://arxiv.org/abs/2110.03968 · HTML: https://mlsanity.com/paper/2110.03968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017)
- [Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://mlsanity.com/paper/1802.05591/) (2018)
- [Semantic Instance Segmentation with a Discriminative Loss Function](https://mlsanity.com/paper/1708.02551/) (2017)
- [TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module](https://mlsanity.com/paper/2008.10544/) (2020)
- [RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving](https://mlsanity.com/paper/2106.02527/) (2021)
- [Convolutional Recurrent Network for Road Boundary Extraction](https://mlsanity.com/paper/2012.12160/) (2020)
- [MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square](https://mlsanity.com/paper/2102.03771/) (2021)
- [Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR](https://mlsanity.com/paper/1907.05375/) (2019)
- [Automatic Vector-based Road Structure Mapping Using Multi-beam LiDAR](https://mlsanity.com/paper/1804.07028/) (2018)

## Built on by (most cited first, 4 of 4)

- [Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles](https://mlsanity.com/paper/2302.08292/) (2023)
- [CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2403.16794/) (2024)
- [Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development](https://mlsanity.com/paper/2309.13596/) (2023)
- [LiDAR-based curb detection for ground truth annotation in automated driving validation](https://mlsanity.com/paper/2312.00534/) (2023)

## Abstract (arXiv)

Curbs are one of the essential elements of urban and highway traffic environments. Robust curb detection provides road structure information for motion planning in an autonomous driving system. Commonly, video cameras and 3D LiDARs are mounted on autonomous vehicles for curb detection. However, camera-based methods suffer from challenging illumination conditions. During the long period of time before wide application of Deep Neural Network (DNN) with point clouds, LiDAR-based curb detection methods are based on hand-crafted features, which suffer from poor detection in some complex scenes. Recently, DNN-based dynamic object detection using LiDAR data has become prevalent, while few works pay attention to curb detection with a DNN approach due to lack of labeled data. A dataset with curb annotations or an efficient curb labeling approach, hence, is of high demand...