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26 dataset results for LiDAR

The nuScenes dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore. Each scene is 20 seconds long and annotated at 2Hz. This results in a total of 28130 samples for training, 6019 samples for validation and 6008 samples for testing. The dataset has the full autonomous vehicle data suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage. The 3D object detection challenge evaluates the performance on 10 classes: cars, trucks, buses, trailers, construction vehicles, pedestrians, motorcycles, bicycles, traffic cones and barriers.

327 PAPERS • 10 BENCHMARKS

Argoverse

Argoverse is a tracking benchmark with over 30K scenarios collected in Pittsburgh and Miami. Each scenario is a sequence of frames sampled at 10 HZ. Each sequence has an interesting object called “agent”, and the task is to predict the future locations of agents in a 3 seconds future horizon. The sequences are split into training, validation and test sets, which have 205,942, 39,472 and 78,143 sequences respectively. These splits have no geographical overlap.

108 PAPERS • 5 BENCHMARKS

Waymo Open Dataset

The Waymo Open Dataset is comprised of high resolution sensor data collected by autonomous vehicles operated by the Waymo Driver in a wide variety of conditions.

95 PAPERS • 8 BENCHMARKS

Newer College

The Newer College Dataset is a large dataset with a variety of mobile mapping sensors collected using a handheld device carried at typical walking speeds for nearly 2.2 km through New College, Oxford. The dataset includes data from two commercially available devices - a stereoscopic-inertial camera and a multi-beam 3D LiDAR, which also provides inertial measurements. Additionally, the authors used a tripod-mounted survey grade LiDAR scanner to capture a detailed millimeter-accurate 3D map of the test location (containing ∼290 million points).

13 PAPERS • NO BENCHMARKS YET

SemanticPOSS

The SemanticPOSS dataset for 3D semantic segmentation contains 2988 various and complicated LiDAR scans with large quantity of dynamic instances. The data is collected in Peking University and uses the same data format as SemanticKITTI.

11 PAPERS • NO BENCHMARKS YET

MVSEC (Multi Vehicle Stereo Event Camera)

The Multi Vehicle Stereo Event Camera (MVSEC) dataset is a collection of data designed for the development of novel 3D perception algorithms for event based cameras. Stereo event data is collected from car, motorbike, hexacopter and handheld data, and fused with lidar, IMU, motion capture and GPS to provide ground truth pose and depth images.

7 PAPERS • NO BENCHMARKS YET

Sydney Urban Objects

This dataset contains a variety of common urban road objects scanned with a Velodyne HDL-64E LIDAR, collected in the CBD of Sydney, Australia. There are 631 individual scans of objects across classes of vehicles, pedestrians, signs and trees.

4 PAPERS • 1 BENCHMARK

Apolloscape Trajectory

Our trajectory dataset consists of camera-based images, LiDAR scanned point clouds, and manually annotated trajectories. It is collected under various lighting conditions and traffic densities in Beijing, China. More specifically, it contains highly complicated traffic flows mixed with vehicles, riders, and pedestrians.

2 PAPERS • 1 BENCHMARK

FieldSAFE

The FieldSAFE dataset is a multi-modal dataset for obstacle detection in agriculture. It comprises 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016.

2 PAPERS • NO BENCHMARKS YET

STF_dense_fog (SeeingThroughFog)

We introduce an object detection dataset in challenging adverse weather conditions covering 12000 samples in real-world driving scenes and 1500 samples in controlled weather conditions within a fog chamber. The dataset includes different weather conditions like fog, snow, and rain and was acquired by over 10,000 km of driving in northern Europe. The driven route with cities along the road is shown on the right. In total, 100k Objekts were labeled with accurate 2D and 3D bounding boxes. The main contributions of this dataset are: - We provide a proving ground for a broad range of algorithms covering signal enhancement, domain adaptation, object detection, or multi-modal sensor fusion, focusing on the learning of robust redundancies between sensors, especially if they fail asymmetrically in different weather conditions. - The dataset was created with the initial intention to showcase methods, which learn of robust redundancies between the sensor and enable a raw data sensor fusion in cas

2 PAPERS • 1 BENCHMARK

UofTPed50

UofTPed50 is an object detection and tracking dataset which uses GPS to ground truth the position and velocity of a pedestrian.

2 PAPERS • NO BENCHMARKS YET

Apolloscape Inpainting

The Inpainting dataset consists of synchronized Labeled image and LiDAR scanned point clouds. It's captured by HESAI Pandora All-in-One Sensing Kit. It is collected under various lighting conditions and traffic densities in Beijing, China.

1 PAPER • 1 BENCHMARK

BAAI-VANJEE

BAAI-VANJEE is a dataset for benchmarking and training various computer vision tasks such as 2D/3D object detection and multi-sensor fusion. The BAAI-VANJEE roadside dataset consists of LiDAR data and RGB images collected by VANJEE smart base station placed on the roadside about 4.5m high. This dataset contains 2500 frames of LiDAR data, 5000 frames of RGB images, including 20% collected at the same time. It also contains 12 classes of objects, 74K 3D object annotations and 105K 2D object annotations.

1 PAPER • NO BENCHMARKS YET

EXPLICIT 3D CHANGE DETECTION USING RAY-TRACING IN SPHERICAL COORDINATES

Real and simulated lidar data of indoor and outdoor scenes, before and after geometric scene changes have occurred. Data include lidar scans from multiple viewpoints with provided coordinate transforms, and manually annotated ground-truth regarding which parts of the scene have changed between subsequent scans.

1 PAPER • NO BENCHMARKS YET

EviLOG (Evidential Lidar Occupancy Grid Mapping)

The dataset contains synthetic training, validation and test data for occupancy grid mapping from lidar point clouds. Additionally, real-world lidar point clouds from a test vehicle with the same lidar setup as the simulated lidar sensor is provided. Point clouds are stored as PCD files and occupancy grid maps are stored as PNG images whereas one image channel describes evidence for a free and another one describes evidence for occupied cell state.

1 PAPER • NO BENCHMARKS YET

Ford Campus Vision and Lidar Data Set

Ford Campus Vision and Lidar Data Set is a dataset collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS LV) and consumer (Xsens MTI-G) Inertial Measuring Unit (IMU), a Velodyne 3D-lidar scanner, two push-broom forward looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system.

1 PAPER • NO BENCHMARKS YET

LiDAR-MOS

LiDAR-MOS (LiDAR-based Moving Object Segmentation)

Tasks. In moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. Therefore, the input to all evaluated methods is a list of coordinates of the three-dimensional points along with their remission, i.e., the strength of the reflected laser beam which depends on the properties of the surface that was hit. Each method should then output a label for each point of a scan, i.e., one full turn of the rotating LiDAR sensor. Here, we only distinguish between static and moving object classes.

1 PAPER • NO BENCHMARKS YET

Near-Collision

Near-Collision is a large-scale dataset of 13,658 egocentric video snippets of humans navigating in indoor hallways. In order to obtain ground truth annotations of human pose, the videos are provided with the corresponding 3D point cloud from LIDAR.

1 PAPER • NO BENCHMARKS YET

ONCE (One Million Scenes)

ONCE (One millioN sCenEs) is a dataset for 3D object detection in the autonomous driving scenario. The ONCE dataset consists of 1 million LiDAR scenes and 7 million corresponding camera images. The data is selected from 144 driving hours, which is 20x longer than other 3D autonomous driving datasets available like nuScenes and Waymo, and it is collected across a range of different areas, periods and weather conditions.

1 PAPER • NO BENCHMARKS YET

RISEdb (Robust Indoor Localization in Complex Scenarios (RISE) database)

The RISE (Robust Indoor Localization in Complex Scenarios) dataset is meant to train and evaluate visual indoor place recognizers. It contains more than 1 million geo-referenced images spread over 30 sequences, covering 5 heterogeneous buildings. For each building we provide: - A high resolution 3D point cloud (1cm) that defines the localization reference frame and that was generated with a mobile laser scanner and an inertial system. - Several image sequences spread over time with accurate ground truth poses retrieved by the laser scanner. Each sequence contains both, stereo pairs and spherical images. - Geo-referenced smartphone data, retrieved from the standard sensors of such devices.

1 PAPER • NO BENCHMARKS YET

THÖR

THÖR is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. THOR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space.

1 PAPER • NO BENCHMARKS YET

WADS (Winter Adverse Driving dataSet)

Collected in the snow belt region of Michigan's Upper Peninsula, WADS is the first multi-modal dataset featuring dense point-wise labeled sequential LiDAR scans collected in severe winter weather.

1 PAPER • NO BENCHMARKS YET

IN2LAAMA

IN2LAAMA is a set of lidar-inertial datasets collected with a Velodyne VLP-16 lidar and a Xsens MTi-3 IMU.

0 PAPER • NO BENCHMARKS YET

Multifog KITTI dataset

we propose the augmented KITTI dataset with fog for both camera and LiDAR sensors with different visibility ranges from 20 to 80 meters to best match realistic fog environment.

0 PAPER • NO BENCHMARKS YET

Robot@Home dataset

The Robot-at-Home dataset (Robot@Home) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms.

0 PAPER • NO BENCHMARKS YET

USYD CAMPUS

USYD CAMPUS is a driving dataset collected by Zhou et al at the University of Sydney (USyd) campus and surroundings. This USYD Campus Dataset contains more than 60 weeks of drives and is continuously updated. It includes multiple sensor modalities (camera, lidar, GPS, IMU, wheel encoder, steering angle, etc.) and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes.

0 PAPER • NO BENCHMARKS YET

Datasets

26 dataset results for LiDAR