The KITTI Vision Benchmark Suite

Stereo techniques have witnessed tremendous
progress over the last decades, yet some aspects
of the problem still remain
challenging today. Striking examples are
reflecting and textureless surfaces which cannot
easily be recovered using
traditional local regularizers. In this paper, we
therefore propose to regularize over larger
distances using object-
category specific disparity proposals (displets)
which we sample using inverse graphics techniques
based on a sparse
disparity estimate and a semantic segmentation of
the image. The proposed displets encode the fact
that objects of
certain categories are not arbitrarily shaped but
typically exhibit regular structures. We
integrate them as non-local
regularizer for the challenging object class
'car' into a superpixel based CRF framework.

Note: We use the new features from "Stereo
Matching by Training a Convolutional Neural
Network to Compare
Image Patches" (accurate version).

Number of Particles: 30
Number of Superpixels: 1000
Number of Iterations: 40
TRWS - Number of Iterations: 200
Particle Standard Deviations: [0.5 0.5 5]

Please check Table 1 of the supplementary material
for a complete list of parameters.

@INPROCEEDINGS{Guney2015CVPR,
author = {Fatma Guney and Andreas Geiger},
title = {Displets: Resolving Stereo Ambiguities
using Object
Knowledge},
booktitle = {Conference on Computer Vision and
Pattern Recognition
(CVPR)},
year = {2015}
}