References
1. List
Lidar Point clound processing for Autonomous Driving
- Clustering/Segmentation (road/ground extraction, plane extraction)
- Registration and Localization
- Feature Extraction
- Object detection and Tracking
- Classification/Supervised Learning
- Maps / Grids / HD Maps / Occupancy grids/ Prior Maps
- End-To-End Learning
- Lidar Datasets and Simulators
Deep Clustering: methods and implements: 추천, 코드 포함
2. Paper
- Vehicle detection from airborne LiDAR point clouds based on a decision tree algorithm with horizontal and vertical features: 엄준호교수, 2017, 필터링, 세그먼트 추출, OBPCA(Object-Based Point Cloud Analysis)기법
- Fast Segmentation of 3D Point Clouds for Ground Vehicles: Himmelsbach2010,깃허브_ROS
- 레이저스캐너 기반 도심 도로 환경 차량 인지/추적 알고리즘 개발: 서울대 김선욱 2017, seonwook2017surrounding
- A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture: 2018
- Learning Neural Models for End-to-End Clustering:2018
- CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data: 2017
3. Article (Post, blog, etc.)
clustering valuation(군집 모델 평가하기)
3. Tutorial (Series, )
4. Youtube
6. Material (Pdf, ppt)
7. Implementation (Project)
- Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications, 2017, 깃허브 : ROS, Cpp
- Deep Clustering for Unsupervised Learning of Visual Features : Caron(facebook), 2018 , [논문], [깃허브]