KITTI BEV Dataset

## Overview

KITTI BEV Dataset is a dataset for object detection tasks - it contains Car Pedestrian Cyclist annotations for 2,559 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Virtual KITTI is a photo-realistic synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation, optical flow, and depth estimation. Virtual KITTI contains 50 high-resolution monocular videos (21,260 frames) generated from five different virtual worlds in urban settings under different imaging and weather conditions. These worlds were created using the Unity game engine and a novel real-to-virtual cloning method. These photo-realistic synthetic videos are automatically, exactly, and fully annotated for 2D and 3D multi-object tracking and at the pixel level with category, instance, flow, and depth labels (cf. below for download links).

The KITTI-Depth dataset includes depth maps from projected LiDAR point clouds that were matched against the depth estimation from the stereo cameras. The depth images are highly sparse with only 5% of the pixels available and the rest is missing. The dataset has 86k training images, 7k validation images, and 1k test set images on the benchmark server with no access to the ground truth.

Dataset

This dataset was created by prashant1312

Released under GNU Free Documentation License 1.3

Contents

KITTI Road is road and lane estimation benchmark that 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 Ground truth has been generated by manual annotation of the images and is available for two different road terrain types: road - the road area, i.e, the composition of all lanes, and lane - the ego-lane, i.e., the lane the vehicle is currently driving on (only available for category "um"). Ground truth is provided for training images only.

The odometry benchmark consists of 22 stereo sequences, saved in loss less png format: We provide 11 sequences (00-10) with ground truth trajectories for training and 11 sequences (11-21) without ground truth for evaluation. For this benchmark you may provide results using monocular or stereo visual odometry, laser-based SLAM or algorithms that combine visual and LIDAR information. The only restriction we impose is that your method is fully automatic (e.g., no manual loop-closure tagging is allowed) and that the same parameter set is used for all sequences. A development kit provides details about the data format. More details are available at: https://www.cvlibs.net/datasets/kitti/eval_odometry.php.

KITTI 7%

## Overview

KITTI 7% is a dataset for object detection tasks - it contains Van PNaz annotations for 4,675 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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).

Kitty

## Overview

Kitty is a dataset for object detection tasks - it contains Car annotations for 7,585 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by eeemmm1234

Contents

Quantitative comparison on KITTI DC validation set.

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/

Processed versions of some open-source datasets for evaluation of monocular geometry estimation.

Dataset Source Publication Num images Storage Size Note

NYUv2 NYU Depth Dataset V2 [1] 654 243 MB Offical test split. Mirror, glass and window manually removed. Depth beyound 5 m truncated.

KITTI KITTI Vision Benchmark Suite [2, 3] 652 246 MB Eigen's test split.

ETH3D ETH3D SLAM & Stereo Benchmarks [4] 454 1.3 GB Downsized from 6202×4135 to 2048×1365

iBims-1 iBims-1 (independent… See the full description on the dataset page: https://huggingface.co/datasets/Ruicheng/monocular-geometry-evaluation.

The effectiveness of different components.