The KITTI Vision Benchmark Suite

Road pixels were classified using a neural network. The classification was done on superpixels produced for each image by spsstereo (http://ttic.uchicago.edu/~dmcallester/SPS/index.html). The feature vector for each superpixel consisted of the mean RGB color values, the average 2D position of the pixels and the average 3D position (derived from the disparity provided by spsstereo), the pitch and roll angles relative to the camera of the plane fit to the superpixel (using ransac), a flag as to whether the average 2D position was above the horizon line derived from the camera parameters, and the standard deviation of both the color values and 3D position. The network was trained in MATLAB using the neural network toolbox on the 289 training images. A plane was then fit using ransac to all network labelled road pixels. Outliers of this plane had their label confidence cut in half. The resulting labels were then smoothed using a dense CRF (http://graphics.stanford.edu/projects/densecrf/).

\superpixels=3000

@inproceedings{Chen2015NIPS,
title = {3D Object Proposals for Accurate Object
Class Detection},
author = {Xiaozhi Chen and Kaustav Kundu and Yukun
Zhu and Andrew Berneshawi and Huimin Ma and Sanja
Fidler and Raquel Urtasun},
booktitle = {NIPS},
year = {2015}
}