The KITTI Vision Benchmark Suite

We apply the sliding window paradigm to pure 3D data,
no information from the images is used. Efficiency is
achieved via conducting the sliding window search in the
form of voting. 3D detections are then projected into the
image for the sake of evaluation with the KITTI vision
benchmark only.

grid_resolution = 0.2m, num_angular_bins = 8,
overlap_threshold = 0.01 (Car) 0.1 (Cyclist) 0.5 (Pedestrian)

@inproceedings{Wang2015RSS,
author = {Dominic Zeng Wang and Ingmar Posner},
title = {Voting for Voting in Online Point Cloud Object
Detection},
booktitle = {Proceedings of Robotics: Science and
Systems},
year = {2015},
address = {Rome, Italy},
month = {July}
}