Dataset

This dataset was created by Sumanyu Ghoshal

Contents

The depth prediction evaluation is related to the work published in Sparsity Invariant CNNs (THREEDV 2017). It contains over 93 thousand depth maps with corresponding raw LiDaR scans and RGB images, aligned with the "raw data" of the KITTI dataset. Given a large amount of training data, this dataset shall allow the training of complex deep learning models for depth completion and single image depth prediction tasks. Also, manually selected images with unpublished depth maps are provided here to serve as a benchmark for those two challenging tasks.

Mentions:

• An original paper where this dataset is first mentioned - "Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite".
• The paper "Vision meets Robotics: The KITTI Dataset" describes details about the dataset and its collection.
• This depth-estimation dataset is also mentioned in Depth Anything V1 and Depth Anything V2 papers.

This dataset consists of four ZIP files containing annotated images used for experiments in the research of formal specification and specification-based testing for image recognition in autonomous driving systems. The dataset has been derived and modified from the KITTI dataset.

• image1.zip: Contains 349 images. These images are part of the first subset used in the experiments.

• label1.zip: Contains the 2D bounding box annotations for vehicles corresponding to the images in image1.zip. There are 349 annotation files, and in total, 2,736 vehicles are annotated.

• image2.zip: Contains 1,300 images. These images are part of the second subset used in the experiments.

• label2.zip: Contains the 2D bounding box annotations for vehicles corresponding to the images in image2.zip. There are 1,300 annotation files, and in total, 5,644 vehicles are annotated.

The dataset was utilized in the research project focused on Bounding Box Specification Language (BBSL), a formal specification language designed for image recognition in autonomous driving systems. This research explores specification-based testing methodologies for object detection systems.

The BBSL project and related testing tools can be accessed on GitHub: https://github.com/IOKENTOI/BBSL-test.

The original KITTI dataset used for modification can be found at [KITTI dataset source link].
If you use this dataset, please cite the original KITTI dataset:
@inproceedings{Geiger2012CVPR,
author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2012}
}

KITTI Closest Vehicle Distance (Test set only)

Original paper: Vision meets Robotics: The KITTI Dataset Homepage: https://www.cvlibs.net/datasets/kitti/ Bibtex: @ARTICLE{Geiger2013IJRR, author = {Andreas Geiger and Philip Lenz and Christoph Stiller and Raquel Urtasun}, title = {Vision meets Robotics: The KITTI Dataset}, journal = {International Journal of Robotics Research (IJRR)}, year = {2013} }

In this dataset, you will find two varieties; one from the benchmark KITTI dataset that contains complex road scenes and a high level of occlusion and truncation in road objects. On the other hand, the Waymo dataset gives versatile scenes from extreme weather and night timings. All relevant information regarding data usage is given in the readMe.txt file.

In KITTI all frames are saved in one place with different scenes; however, for Waymo, we have collected data from different segments to make the variety so data can be seen in 12 different folders with all necessary files. Around 2400 frames are considered from each dataset.

Data is collected from two sensors camera and LiDAR. For information about labeling format, calibration and mapping, please check readMe.txt.

For complete KITTI dataset please see the link http://www.cvlibs.net/datasets/kitti/raw_data.php. For complete Waymo dataset please see the link https://waymo.com/intl/en_us/dataset-download-terms

This dataset is created by DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation, based on KITTI and nuScenes. You can check this link for more details: https://www.arxiv.org/abs/2503.11122 And access the code: https://github.com/Hongbin98/DriveGEN Please double-check the demands of KITTI and nuScenes when you try to download this dataset and obey their rules.

https://www.cvlibs.net/datasets/kitti/ https://www.nuscenes.org/

This Datasets contains the Kitti Object Detection Benchmark, created by Andreas Geiger, Philip Lenz and Raquel Urtasun in the Proceedings of 2012 CVPR ," Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite". This Kernel contains the object detection part of their different Datasets published for Autonomous Driving. It contains a set of images with their bounding box labels and velodyne point clouds. For more information visit the Website they published the data on (http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d).

ground truth

Introduction

Light Detection And Ranging (LiDAR) is a sensor that is used to measure distances between the sensor and the surroundings. It depends on sending multiple laser beams and sense them back after being reflected to calculate the distance between the sensor and the objected they were reflected on. Since the rise of the research in the field of self-driving cars, LiDAR has been widely used and was even developed to be with lower cost than before.

KITTI dataset is one of the most famous datasets targeting the field of self-driving cars. It contains recorded data from camera, LiDAR and other sensors mounted on top of a car that moves in many streets with many different scenes and scenarios.

This dataset contains the LiDAR frames of KITTI dataset converted to 2D depth images and it was converted using this code. These 2D depth images represents the same scene of the corresponding LiDAR frame but in an easier to process format.

Content

This dataset contains 2D depth images, like the one represented below. The 360 LiDAR frames like those in KITTI dataset are in a cylindrical format around the sensor itself. The 2D depth images in this dataset could be represented as if you have made a cut in the cylinder of the LiDAR frame and straightened it to be in a 2D plane. The pixels of these 2D depth images represent the distance of the reflecting object from the LiDAR sensor. The vertical resolution of the 2D depth image (64 in our case) represents the number of laser beams of the LiDAR sensor used to scan the surroundings. These 2D depth images could be used for segmentation, detection, recognition and etc. tasks and could make use of the huge literature of computer vision on 2D images. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3283916%2F71fcde75b3e94ab78896aa75d7efea09%2F0000000077.png?generation=1595578603898080&alt=media" alt="">

kitti-Road/Lane Detection Evaluation 2013

this dataset is from kitti-Road/Lane Detection Evaluation 2013.

This benchmark has been created in collaboration with Jannik Fritsch and Tobias Kuehnl from Honda Research Institute Europe GmbH. The road and lane estimation benchmark consists of 289 training and 290 test images. It contains three different categories of road scenes:

• uu - urban unmarked (98/100)
• um - urban marked (95/96)
• umm - urban multiple marked lanes (96/94)
• urban - combination of the three above