LiDAR-CS is a dataset for 3D object detection in real traffic. It contains 84,000 point cloud frames under 6 groups of different sensors but with same corresponding scenarios, captured from hybrid realistic LivDAR simulator.
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The Argoverse 2 Motion Forecasting Dataset is a curated collection of 250,000 scenarios for training and validation. Each scenario is 11 seconds long and contains the 2D, birds-eye-view centroid and heading of each tracked object sampled at 10 Hz.
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TAS-NIR is a VIS+NIR dataset of semantically annotated images in unstructured outdoor environments. It consists of 209 VIS+NIR image pairs with a fine-grained semantic segmentation.
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The Autonomous-driving StreAming Perception (ASAP) benchmark is a benchmark to evaluate the online performance of vision-centric perception in autonomous driving. It extends the 2Hz annotated nuScenes dataset by generating high-frame-rate labels for the 12Hz raw images.
The Apron Dataset focuses on training and evaluating classification and detection models for airport-apron logistics. In addition to bounding boxes and object categories the dataset is enriched with meta parameters to quantify the models’ robustness against environmental influences.
Situated Dialogue Navigation (SDN) is a navigation benchmark of 183 trials with a total of 8415 utterances, around 18.7 hours of control streams, and 2.9 hours of trimmed audio. SDN is developed to evaluate the agent's ability to predict dialogue moves from humans as well as generate its own dialogue moves and physical navigation actions.
The MUAD dataset (Multiple Uncertainties for Autonomous Driving), consisting of 10,413 realistic synthetic images with diverse adverse weather conditions (night, fog, rain, snow), out-of-distribution objects, and annotations for semantic segmentation, depth estimation, object, and instance detection. Predictive uncertainty estimation is essential for the safe deployment of Deep Neural Networks in real-world autonomous systems and MUAD allows to a better assess the impact of different sources of uncertainty on model performance.
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SODA-D is a large-scale dataset tailored for small object detection in driving scenario, which is built on top of MVD dataset and owned data, where the former is a dataset dedicated to pixel-level understanding of street scenes, and the latter is mainly captured by onboard cameras and mobile phones. With 24704 well-chosen and high-quality images of driving scenarios, SODA-D comprises 277596 instances of 9 categories with horizontal bounding boxes.
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Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised Domain Adaptation has been applied to a wide variety of complex vision tasks, only few works focus on lane detection for autonomous driving. This can be attributed to the lack of publicly available datasets. To facilitate research in these directions, we propose CARLANE, a 3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE encompasses the single-target datasets MoLane and TuLane and the multi-target dataset MuLane. These datasets are built from three different domains, which cover diverse scenes and contain a total of 163K unique images, 118K of which are annotated. In addition we evaluate and report systematic baselines, including our own method, which builds upon Prototypical Cross-domain Self-supervised Learning. We find that false positive and false negative rates of the eva
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Panoramic Video Panoptic Segmentation Dataset is a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving. The dataset has labels for 28 semantic categories and 2,860 temporal sequences that were captured by five cameras mounted on autonomous vehicles driving in three different geographical locations, leading to a total of 100k labeled camera images.
ONCE-3DLanes is a real-world autonomous driving dataset with lane layout annotation in 3D space. A dataset annotation pipeline is designed to automatically generate high-quality 3D lane locations from 2D lane annotations by exploiting the explicit relationship between point clouds and image pixels in 211,000 road scenes.
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SynWoodScape is a synthetic version of the surround-view dataset covering many of its weaknesses and extending it. WoodScape comprises four surround-view cameras and nine tasks, including segmentation, depth estimation, 3D bounding box detection, and a novel soiling detection. Semantic annotation of 40 classes at the instance level is provided for over 10,000 images. With WoodScape, we would like to encourage the community to adapt computer vision models for the fisheye camera instead of using naive rectification.
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PandaSet is a dataset produced by a complete, high-precision autonomous vehicle sensor kit with a no-cost commercial license. The dataset was collected using one 360x360 mechanical spinning LiDAR, one forward-facing, long-range LiDRAR, and 6 cameras. The datasets contains more than 100 scenes, each of which is 8 seconds long, and provides 28 types of labels for object classification and 37 types of annotations for semantic segmentation.
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DurLAR is a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery for multi-modal autonomous driving applications. Compared to existing autonomous driving task datasets, DurLAR has the following novel features:
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METEOR is a complex traffic dataset which captures traffic patterns in unstructured scenarios in India. METEOR consists of more than 1000 one-minute video clips, over 2 million annotated frames with ego-vehicle trajectories, and more than 13 million bounding boxes for surrounding vehicles or traffic agents. METEOR is a unique dataset in terms of capturing the heterogeneity of microscopic and macroscopic traffic characteristics.
The Reasonable Crowd dataset is a dataset to evaluate autonomous driving in a limited operating domain. The data consists of 92 traffic scenarios, with multiple ways of traversing each scenario. Multiple annotators expressed their preference between pairs of scenario traversals.
SODA10M is a large-scale object detection benchmark for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data. SODA10M contains 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes.
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ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event.
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UNDD consists of 7125 unlabelled day and night images; additionally, it has 75 night images with pixel-level annotations having classes equivalent to Cityscapes dataset.
ELAS is a dataset for lane detection. It contains more than 20 different scenes (in more than 15,000 frames) and considers a variety of scenarios (urban road, highways, traffic, shadows, etc.). The dataset was manually annotated for several events that are of interest for the research community (i.e., lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes).
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ApolloScape is a large dataset consisting of over 140,000 video frames (73 street scene videos) from various locations in China under varying weather conditions. Pixel-wise semantic annotation of the recorded data is provided in 2D, with point-wise semantic annotation in 3D for 28 classes. In addition, the dataset contains lane marking annotations in 2D.
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CARLA (CAR Learning to Act) is an open simulator for urban driving, developed as an open-source layer over Unreal Engine 4. Technically, it operates similarly to, as an open source layer over Unreal Engine 4 that provides sensors in the form of RGB cameras (with customizable positions), ground truth depth maps, ground truth semantic segmentation maps with 12 semantic classes designed for driving (road, lane marking, traffic sign, sidewalk and so on), bounding boxes for dynamic objects in the environment, and measurements of the agent itself (vehicle location and orientation).
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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.
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.
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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.
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H3D (Humans in 3D) is a dataset of annotated people. The annotations include:
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4Seasons is adataset covering seasonal and challenging perceptual conditions for autonomous driving.
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The A*3D dataset is a step forward to make autonomous driving safer for pedestrians and the public in the real world. Characteristics: * 230K human-labeled 3D object annotations in 39,179 LiDAR point cloud frames and corresponding frontal-facing RGB images. * Captured at different times (day, night) and weathers (sun, cloud, rain).
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AirSim is a simulator for drones, cars and more, built on Unreal Engine. It is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. Similarly, there exists an experimental version for a Unity plugin.
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ApolloCar3DT is a dataset that contains 5,277 driving images and over 60K car instances, where each car is fitted with an industry-grade 3D CAD model with absolute model size and semantically labelled keypoints. This dataset is above 20 times larger than PASCAL3D+ and KITTI, the current state-of-the-art.
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Berkeley Deep Drive-X (eXplanation) is a dataset is composed of over 77 hours of driving within 6,970 videos. The videos are taken in diverse driving conditions, e.g. day/night, highway/city/countryside, summer/winter etc. On average 40 seconds long, each video contains around 3-4 actions, e.g. speeding up, slowing down, turning right etc., all of which are annotated with a description and an explanation. Our dataset contains over 26K activities in over 8.4M frames.
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BLVD is a large scale 5D semantics dataset collected by the Visual Cognitive Computing and Intelligent Vehicles Lab. This dataset contains 654 high-resolution video clips owing 120k frames extracted from Changshu, Jiangsu Province, China, where the Intelligent Vehicle Proving Center of China (IVPCC) is located. The frame rate is 10fps/sec for RGB data and 3D point cloud. The dataset contains fully annotated frames which yield 249,129 3D annotations, 4,902 independent individuals for tracking with the length of overall 214,922 points, 6,004 valid fragments for 5D interactive event recognition, and 4,900 individuals for 5D intention prediction. These tasks are contained in four kinds of scenarios depending on the object density (low and high) and light conditions (daytime and nighttime).
This self-driving dataset collected in Brno, Czech Republic contains data from four WUXGA cameras, two 3D LiDARs, inertial measurement unit, infrared camera and especially differential RTK GNSS receiver with centimetre accuracy.
CARRADA is a dataset of synchronized camera and radar recordings with range-angle-Doppler annotations.
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CITR Dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration.
DAWN emphasizes a diverse traffic environment (urban, highway and freeway) as well as a rich variety of traffic flow. The DAWN dataset comprises a collection of 1000 images from real-traffic environments, which are divided into four sets of weather conditions: fog, snow, rain and sandstorms. The dataset is annotated with object bounding boxes for autonomous driving and video surveillance scenarios. This data helps interpreting effects caused by the adverse weather conditions on the performance of vehicle detection systems.
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DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface.
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DR(eye)VE is a large dataset of driving scenes for which eye-tracking annotations are available. This dataset features more than 500,000 registered frames, matching ego-centric views (from glasses worn by drivers) and car-centric views (from roof-mounted camera), further enriched by other sensors measurements.
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DrivingStereo contains over 180k images covering a diverse set of driving scenarios, which is hundreds of times larger than the KITTI Stereo dataset. High-quality labels of disparity are produced by a model-guided filtering strategy from multi-frame LiDAR points.
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The H3D is a large scale full-surround 3D multi-object detection and tracking dataset. It is gathered from HDD dataset, a large scale naturalistic driving dataset collected in San Francisco Bay Area. H3D consists of following features:
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Honda Research Institute Driving Dataset (HDD) is a dataset to enable research on learning driver behavior in real-life environments. The dataset includes 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors.
IDD is a dataset for road scene understanding in unstructured environments used for semantic segmentation and object detection for autonomous driving. It consists of 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads.
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The INTERACTION dataset contains naturalistic motions of various traffic participants in a variety of highly interactive driving scenarios from different countries. The dataset can serve for many behavior-related research areas, such as
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Long-term visual localization provides a benchmark datasets aimed at evaluating 6 DoF pose estimation accuracy over large appearance variations caused by changes in seasonal (summer, winter, spring, etc.) and illumination (dawn, day, sunset, night) conditions. Each dataset consists of a set of reference images, together with their corresponding ground truth poses, and a set of query images.
Lost and Found is a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic.
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Occ-Traj120 is a trajectory dataset that contains occupancy representations of different local-maps with associated trajectories. This dataset contains 400 locally-structured maps with occupancy representation and roughly around 120K trajectories in total.
The PREdiction of Clinical Outcomes from Genomic profiles (or PRECOG) encompasses 166 cancer expression data sets, including overall survival data for ~18,000 patients diagnosed with 39 distinct malignancies.
PedX is a large-scale multi-modal collection of pedestrians at complex urban intersections. The dataset provides high-resolution stereo images and LiDAR data with manual 2D and automatic 3D annotations. The data was captured using two pairs of stereo cameras and four Velodyne LiDAR sensors.