The KITTI Vision Benchmark Suite

A vehicle detection system based on the
Hypothesis Generation and Verification
paradigms is proposed. The data inputted to the
system is a point cloud obtained from a 3D-
LIDAR mounted on board an instrumented vehicle,
which is then transformed to a dense-Depth Map
(DM). Specifically, the DBSCAN clustering is
used to extract structures (i.e., to segment
individual obstacles that stand on the ground)
from the 3D-LIDAR data to form class-agnostic
object hypotheses, followed by (class-specific)
ConvNet-based classification of such hypotheses
(in the form of a DM).

Grid size (0.5 m), Variance (0.01 m), Minimum # of
points (5) and Distance metric (0.5 m)

@inproceedings{asvadi2017depthcn,
title={DepthCN: vehicle detection using 3D-
LIDAR and convnet},
author={Asvadi, Alireza and Garrote,
Lu{\'\i}s and Premebida, Cristiano and Peixoto,
Paulo and Nunes, Urbano J},
booktitle={IEEE ITSC},
year={2017}
}