We proposed in [1] an integrated object detection and classification pipeline using deep learning techniques for extracting various object categories in continuously streamed LIDAR point clouds collected from urban areas.
A public benchmark database called SZTAKI-Velo64Road is also released for evaluating object recognition algorithms in urban environments based on real time Lidar measurements of a Velodyne HDL 64-E sensor.
3D WebGL demo on object detection and classifaction
Object detection and recognition in a street scenario with four object classes: pedestrian, street clutter, vehicle and short facade .
WebGL demo's authors: A. Börcs, B. Nagy, G. Sepovics and C. Benedek
Reference
[1] A. Börcs, B. Nagy, and C. Benedek: "Instant Object Detection in Lidar Point Clouds," IEEE Geoscience and Remote Sensing Letters, to appear, 2017