CATS 2: Color And Thermal Stereo Scenes with Semantic Labels

Wayne Treible, Philip Saponaro, Yi Liu, Agnijit Das Gupta, Vinit Veerendraveer, Scott Sorensen, Chandra Kambhamettu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 6-10

Abstract

The CATS dataset introduced a new set of diverse indoor and outdoor scenes with ground truth disparity information for testing stereo matching algorithms in color and thermal imagery. These scenes included nighttime, foggy, low light, and complex lighting in scenes. To extend the usefulness of the CATS dataset we add pixel- and instance-level semantic labels. This includes labels for both color and thermal imagery, and the labels also apply to 3D point clouds as a result of the existing 2D-3D alignment. We compare the new CATS 2.0 dataset against other similar datasets and show it is similar in scope to the KITTI-360 and WildDash datasets, but with the addition of both thermal and 3D information. Additionally, we run a benchmark pedestrian detection algorithm on a set of scenes containing pedestrians.

Related Material

[pdf]
[bibtex]
@InProceedings{Treible_2019_CVPR_Workshops,
author = {Treible, Wayne and Saponaro, Philip and Liu, Yi and Das Gupta, Agnijit and Veerendraveer, Vinit and Sorensen, Scott and Kambhamettu, Chandra},
title = {CATS 2: Color And Thermal Stereo Scenes with Semantic Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}