useful links

README.md

@@ -15,6 +15,7 @@ A list of references on lidar point cloud processing for autonomous driving
 * A Comparative Study of Segmentation and Classification Methods for 3D Point Clouds [[pdf](http://publications.lib.chalmers.se/records/fulltext/238602/238602.pdf)]
 * Fast Multi-pass 3D Point Segmentation Based on a Structured Mesh Graph for Ground Vehicles [pdf](https://ieeexplore.ieee.org/abstract/document/8500552) [video](https://www.youtube.com/watch?v=cwmcuRnWJfE)
 * Circular Convolutional Neural Networks for Panoramic Images and Laser Data [pdf](https://www.tu-chemnitz.de/etit/proaut/publications/schubert19_IV.pdf)
+* Python bindings for Point Cloud Library [[git](https://github.com/strawlab/python-pcl)] 
 
 ## Pointcloud Density 
 * DBSCAN algorithm : [wiki](https://en.wikipedia.org/wiki/DBSCAN), A density-based algorithm for discovering clusters in large spatial databases with noise (1996) [pdf[(https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.9220)
@@ -94,6 +95,8 @@ A list of references on lidar point cloud processing for autonomous driving
 * Udacity Driving Dataset [[link](https://github.com/udacity/self-driving-car/tree/master/datasets)]
 * Virtual KITTI [[link](http://www.europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds)]
 * LiDAR-Video Driving Dataset: Learning Driving Policies Effectively [[pdf](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_LiDAR-Video_Driving_Dataset_CVPR_2018_paper.pdf)]
+* KAIST Complex Urban Data Set Dataset [[dataset](http://irap.kaist.ac.kr/dataset/download_1.html)]
+
 
 ## Movement and flow estimation in Pointclouds
 * Deep Lidar CNN to Understand the Dynamics of Moving Vehicles [pdf](http://www.iri.upc.edu/files/scidoc/2018-Deep-Lidar-CNN-to-Understand-the-Dynamics-of-Moving-Vehicles.pdf)