The KITTI Vision Benchmark Suite

This is a computationally efficient approach to detecting
objects natively in 3D point clouds using convolutional neural
networks (CNNs). This is achieved by leveraging a feature-
centric voting scheme to implement novel convolutional layers
which explicitly exploit the sparsity encountered in the input.

Described in more detailed at: http://arxiv.org/abs/1609.06666

3 layer models for cyclists and pedestrians, 2 layer model for
cars.

@ARTICLE{Engelcke2016ARXIV,
author = {{Engelcke}, M. and {Rao}, D. and {Zeng Wang}, D.
and {Hay Tong}, C. and {Posner}, I.},
title = "{Vote3Deep: Fast Object Detection in 3D Point
Clouds Using Efficient Convolutional Neural Networks}",
journal = {ArXiv e-prints},
year = 2016}
}