The Trust, but Verify (TbV) Dataset consists of 1043 vehicle logs. Each vehicle log, on average, is 54 seconds in duration, including 536 LiDAR sweeps on average, and 1073 images from each of the 7 cameras (7512 images per log). Some logs are as short as 4 seconds, and other logs are up to 117 seconds in duration.
The total dataset amounts to 15.54 hours of driving data, amounting to 922 GB of data in its extracted form. There are 7.84 Million images in the dataset (7,837,614 exactly), and 559,440 LiDAR sweeps in total.
TbV is available for download in two forms -- either zipped up as 21 tar.gz files -- or in extracted, unzipped form (without tar archives). Downloading either will produce the same result (the underlying log data is identical).
Using the tar.gz files is recommended (depending upon your connection, this is likely faster, as there are almost 8 million images files in the extracted format). We recommend using s5cmd to pull down all 21 .tar.gz files with a single command. You can see the links to the tar.gz files on the Argoverse 2 downloads page.
First, install s5cmd using the installation instructions here, and then download the 21 tar.gz archives from Amazon S3 as follows:
SHARD_DIR={DESIRED PATH FOR TAR.GZ files}
s5cmd --no-sign-request cp s3://argoai-argoverse/av2/tars/tbv/*.tar.gz ${SHARD_DIR}If you would prefer to not install a 3rd party download tool (s5cmd), you can use wget to download the tar.gz files:
wget https://s3.amazonaws.com/argoai-argoverse/av2/tars/tbv/TbV_v1.0_shard0.tar.gz
wget https://s3.amazonaws.com/argoai-argoverse/av2/tars/tbv/TbV_v1.0_shard1.tar.gz
...
wget https://s3.amazonaws.com/argoai-argoverse/av2/tars/tbv/TbV_v1.0_shard20.tar.gzNext, extract TbV tar.gz files that were just downloaded to a local disk using untar_tbv.py:
python tutorials/untar_tbv.pyNot Recommended: If you want to directly transfer the extracted files, you may use:
DESIRED_TBV_DATAROOT={DESIRED LOCAL DIRECTORY PATH FOR TBV VEHICLE LOGS}
s5cmd --no-sign-request cp s3://argoai-argoverse/av2/tbv/* ${DESIRED_TBV_DATAROOT}TbV vehicle logs are captured in 6 cities, according to the following distribution:
- Austin, Texas: 80 logs.
- Detroit, Michigan: 139 logs.
- Miami, Florida: 349 logs.
- Pittsburgh, Pennsylvania: 318 logs.
- Palo Alto, California: 21 logs.
- Washington, D.C.: 136 logs.
We provide both pre-trained models for HD map change detection and code for training such models at https://github.com/johnwlambert/tbv.
The sensor suite is identical to the Argoverse 2 Sensor Dataset, except no stereo sensor data is provided, and the sensor imagery for 6 of the cameras is provided at half of the image resolution (ring_front_center is at an identical resolution, however).
Lidar sweeps are collected at 10 Hz, along with 20 fps imagery from 7 ring cameras positioned to provide a fully panoramic field of view. In addition, camera intrinsics, extrinsics and 6-DOF ego-vehicle pose in a global coordinate system are provided. Lidar returns are captured by two 32-beam lidars, spinning at 10 Hz in the same direction, but separated in orientation by 180°. The cameras trigger in-sync with both lidars, leading to a 20 Hz frame-rate. The seven global shutter cameras are synchronized to the lidar to have their exposure centered on the lidar sweeping through their fields of view.
We aggregate all returns from the two stacked 32-beam sensors into a single sweep. These sensors each have different, overlapping fields-of-view. Both lidars have their own reference frame, and we refer to them as up_lidar and down_lidar, respectively. We have egomotion-compensated the LiDAR sensor data to the egovehicle reference nanosecond timestamp. All LiDAR returns are provided in the egovehicle reference frame, not the individual LiDAR reference frame.
TbV imagery is provided at (height x width) of 2048 x 1550 (portrait orientation) for the ring front-center camera, and at 775 x 1024 (landscape orientation) for all other 6 cameras. Please note that the ring front-center camera imagery is provided at higher resolution. All camera imagery is provided in an undistorted format.
Tabular data (lidar sweeps, poses, calibration) are provided as Apache Feather Files with the file extension .feather. We show examples below.
Unlike the Argoverse 2 Sensor Dataset, TbV features no object annotations.
A local vector map and a local ground height raster map is provided per log, please refer to the Map README for additional details. For example, for log VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020, the map subdirectory contains 3 files:
log_map_archive_VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020____DTW_city_73942.json: local vector map.VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020_ground_height_surface____DTW.npy: local ground height raster map, at 30 cm resolution.VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020___img_Sim2_city.json: mapping from city coordinates to raster grid/array coordinates.
6-DOF ego-vehicle pose in a global (city) coordinate system is provided (visualized in the figure below as a red line, with red circles indicated at a 1 Hz frequency):
We refer to this pose as city_SE3_egovehicle throughout the codebase:
>>> import av2.utils.io as io_utils
>>> io_utils.read_feather("{TBV_ROOT}/VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020/city_SE3_egovehicle.feather")
timestamp_ns qw qx qy qz tx_m ty_m tz_m
0 315969466027482498 0.245655 0.009583 -0.014121 -0.969207 9277.579933 6805.407468 -22.647127
1 315969466042441191 0.245661 0.009824 -0.014529 -0.969197 9277.496340 6805.362364 -22.647355
2 315969466057428264 0.245682 0.009999 -0.015003 -0.969183 9277.418457 6805.317208 -22.648150
3 315969466060265000 0.245687 0.010025 -0.015133 -0.969179 9277.402699 6805.308645 -22.648235
4 315969466077482496 0.245723 0.010218 -0.015682 -0.969159 9277.306645 6805.257303 -22.648716
... ... ... ... ... ... ... ... ...
8811 315969525887425441 0.843540 0.008404 -0.005364 -0.536974 9371.218847 6465.181151 -23.095571
8812 315969525892441193 0.843547 0.008349 -0.005421 -0.536963 9371.243129 6465.129394 -23.097279
8813 315969525899927216 0.843569 0.008234 -0.005435 -0.536930 9371.278003 6465.054774 -23.097989
8814 315969525907428274 0.843575 0.008092 -0.005358 -0.536924 9371.312815 6464.980204 -23.098440
8815 315969525912451243 0.843601 0.008013 -0.005400 -0.536883 9371.333643 6464.934933 -23.095809
[8816 rows x 8 columns]For example, we show below the format of an example sweep sensors/lidar/315969468259945000.feather (the sweep has a reference timestamp of 315969468259945000 nanoseconds). Unlike the sensor dataset, TbV sweeps do not contain timestamps per return (there is no offset_ns attribute):
>>> io_utils.read_feather("{TBV_ROOT}/VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020/sensors/lidar/315969468259945000.feather")
x y z intensity laser_number
0 -13.023438 12.492188 -0.138794 103 25
1 -10.992188 10.726562 1.831055 36 7
2 -15.273438 14.460938 0.356445 35 23
3 -10.828125 10.609375 1.076172 49 19
4 -10.570312 10.421875 1.456055 104 3
... ... ... ... ... ...
89261 4.136719 -2.882812 1.631836 0 19
89262 4.054688 -2.783203 1.546875 23 3
89263 60.312500 -77.937500 10.671875 47 25
89264 17.984375 -21.390625 1.214844 6 7
89265 4.160156 -2.953125 1.719727 36 23
[89266 rows x 5 columns]An example calibration file is shown below, parameterizing vehicle_SE3_sensor for each sensor (the sensor's pose in the egovehicle coordinate system):
>>> io_utils.read_feather("{TBV_ROOT}/VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020/calibration/egovehicle_SE3_sensor.feather")
sensor_name qw qx qy qz tx_m ty_m tz_m
0 ring_front_center 0.501067 -0.499697 0.501032 -0.498200 1.626286 -0.020252 1.395709
1 ring_front_left 0.635731 -0.671186 0.277021 -0.261946 1.549577 0.177582 1.388212
2 ring_front_right 0.262148 -0.277680 0.670922 -0.635638 1.546437 -0.216452 1.394248
3 ring_rear_left 0.602832 -0.602666 -0.368113 0.371322 1.099130 0.106534 1.389519
4 ring_rear_right 0.371203 -0.367863 -0.601619 0.604103 1.101165 -0.141049 1.399768
5 ring_side_left 0.686808 -0.722414 -0.058060 0.055145 1.308706 0.255756 1.379285
6 ring_side_right 0.055626 -0.056105 -0.722917 0.686403 1.306407 -0.291250 1.394200
7 up_lidar 0.999995 0.000000 0.000000 -0.003215 1.350110 -0.013707 1.640420
8 down_lidar 0.000080 -0.994577 0.103998 0.000039 1.355172 -0.021696 1.507259An example camera intrinsics file is shown below:
>>> io_utils.read_feather("{TBV_ROOT}/VvgE5LfOzIahbS266MFW7tP2al00LhQn__Autumn_2020/calibration/intrinsics.feather")
sensor_name fx_px fy_px cx_px cy_px k1 k2 k3 height_px width_px
0 ring_front_center 1686.020228 1686.020228 775.467979 1020.785939 -0.245028 -0.196287 0.301861 2048 1550
1 ring_front_left 842.323546 842.323546 513.397368 387.828521 -0.262302 -0.108561 0.179488 775 1024
2 ring_front_right 842.813516 842.813516 514.154170 387.181497 -0.257722 -0.125524 0.199077 775 1024
3 ring_rear_left 841.669682 841.669682 513.211190 387.324359 -0.257018 -0.130649 0.204405 775 1024
4 ring_rear_right 843.832813 843.832813 512.201788 387.673600 -0.256830 -0.132244 0.208272 775 1024
5 ring_side_left 842.178507 842.178507 512.314602 388.188297 -0.256152 -0.131642 0.205564 775 1024
6 ring_side_right 842.703781 842.703781 513.191605 386.876520 -0.260558 -0.110271 0.179140 775 1024All faces and license plates, whether inside vehicles or outside of the drivable area, are blurred extensively to preserve privacy.