KITTI-360 is a large-scale dataset that contains rich sensory information and full annotations. The authors recorded several suburbs of Karlsruhe, Germany, corresponding to over 320k images and 100k laser scans in a driving distance of 73.7km. They annotate both static and dynamic 3D scene elements with rough bounding primitives and transfer this information into the image domain, resulting in dense semantic & instance annotations for both 3D point clouds and 2D images.
Driving distance: 73.7 km, frames: 4x83,000 All frames accurately geolocalized (OpenStreetMap) Semantic label definition consistent with Cityscapes, 19 classes for evaluation Each instance assigned with a consistent instance ID across all frames
@article{Liao2022PAMI,
author = {Yiyi Liao and Jun Xie and Andreas Geiger},
journal = {Pattern Analysis and Machine Intelligence (PAMI)},
title = {{KITTI}-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D},
year = {2022}
}
