Object recognition with deep learning

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


[1] A. Börcs, B. Nagy and Cs. Benedek: "Instant Object Detection in Lidar Point Clouds", IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 7, pp. 992 - 996, 2017, IF: 2.761*



Geo-Information Computing @ Machine Perception Lab.

GeoComp Research Group

Machine Perception Laboratory

SZTAKI main page

GeoComp Demos:

Demo page



GeoComp Group leader: Dr. Csaba Benedek benedek.csaba@sztaki.mta.hu

i4D project manager: Dr. Zsolt Jankó janko.zsolt@sztaki.mta.hu

Head of MPLab: Prof. Tamás Szirányi

MPLab administration: Anikó Vágvölgyi


Kende utca 13-17
H-1111 Budapest, Hungary
Tel: +36 1 279 6194
Fax: +36 1 279 6292

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