The KITTI Vision Benchmark Suite is not hosted by this project nor it's claimed that you have license to use the dataset, it is your responsibility to determine whether you have permission to use this dataset under its license. If you find this code or our dataset helpful in your research, please use the following BibTeX entry. where l=left, r=right, u=up, d=down, f=forward, PointGray Flea2 grayscale camera (FL2-14S3M-C), PointGray Flea2 color camera (FL2-14S3C-C), resolution 0.02m/0.09 , 1.3 million points/sec, range: H360 V26.8 120 m. IJCV 2020. The categorization and detection of ships is crucial in maritime applications such as marine surveillance, traffic monitoring etc., which are extremely crucial for ensuring national security. Creative Commons Attribution-NonCommercial-ShareAlike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/. This should create the file module.so in kitti/bp. Start a new benchmark or link an existing one . None. KITTI-360 is a suburban driving dataset which comprises richer input modalities, comprehensive semantic instance annotations and accurate localization to facilitate research at the intersection of vision, graphics and robotics. See the License for the specific language governing permissions and. its variants. For inspection, please download the dataset and add the root directory to your system path at first: You can inspect the 2D images and labels using the following tool: You can visualize the 3D fused point clouds and labels using the following tool: Note that all files have a small documentation at the top. 1. . Unsupervised Semantic Segmentation with Language-image Pre-training, Papers With Code is a free resource with all data licensed under, datasets/590db99b-c5d0-4c30-b7ef-ad96fe2a0be6.png, STEP: Segmenting and Tracking Every Pixel. The "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation, "Object" form shall mean any form resulting from mechanical, transformation or translation of a Source form, including but. 9. For many tasks (e.g., visual odometry, object detection), KITTI officially provides the mapping to raw data, however, I cannot find the mapping between tracking dataset and raw data. Contributors provide an express grant of patent rights. risks associated with Your exercise of permissions under this License. It is widely used because it provides detailed documentation and includes datasets prepared for a variety of tasks including stereo matching, optical flow, visual odometry and object detection. Cars are marked in blue, trams in red and cyclists in green. Are you sure you want to create this branch? See all datasets managed by Max Planck Campus Tbingen. APPENDIX: How to apply the Apache License to your work. You may reproduce and distribute copies of the, Work or Derivative Works thereof in any medium, with or without, modifications, and in Source or Object form, provided that You, (a) You must give any other recipients of the Work or, Derivative Works a copy of this License; and, (b) You must cause any modified files to carry prominent notices, (c) You must retain, in the Source form of any Derivative Works, that You distribute, all copyright, patent, trademark, and. The establishment location is at 2400 Kitty Hawk Rd, Livermore, CA 94550-9415. The Audi Autonomous Driving Dataset (A2D2) consists of simultaneously recorded images and 3D point clouds, together with 3D bounding boxes, semantic segmentsation, instance segmentation, and data extracted from the automotive bus. OV2SLAM, and VINS-FUSION on the KITTI-360 dataset, KITTI train sequences, Mlaga Urban dataset, Oxford Robotics Car . angle of The files in kitti/bp are a notable exception, being a modified version of Pedro F. Felzenszwalb and Daniel P. Huttenlocher's belief propogation code 1 licensed under the GNU GPL v2. variety of challenging traffic situations and environment types. build the Cython module, run. Licensed works, modifications, and larger works may be distributed under different terms and without source code. You are solely responsible for determining the, appropriateness of using or redistributing the Work and assume any. deep learning To test the effect of the different fields of view of LiDAR on the NDT relocalization algorithm, we used the KITTI dataset with a full length of 864.831 m and a duration of 117 s. The test platform was a Velodyne HDL-64E-equipped vehicle. The label is a 32-bit unsigned integer (aka uint32_t) for each point, where the This dataset contains the object detection dataset, including the monocular images and bounding boxes. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all, other commercial damages or losses), even if such Contributor. You can install pykitti via pip using: Logs. The approach yields better calibration parameters, both in the sense of lower . The business address is 9827 Kitty Ln, Oakland, CA 94603-1071. Business Information For each frame GPS/IMU values including coordinates, altitude, velocities, accelerations, angular rate, accuracies are stored in a text file. grid. temporally consistent over the whole sequence, i.e., the same object in two different scans gets The license expire date is December 31, 2022. not limited to compiled object code, generated documentation, "Work" shall mean the work of authorship, whether in Source or, Object form, made available under the License, as indicated by a, copyright notice that is included in or attached to the work. The expiration date is August 31, 2023. . your choice. particular, the following steps are needed to get the complete data: Note: On August 24, 2020, we updated the data according to an issue with the voxelizer. Copyright [yyyy] [name of copyright owner]. Semantic Segmentation Kitti Dataset Final Model. Visualising LIDAR data from KITTI dataset. 5. The training labels in kitti dataset. Our dataset is based on the KITTI Vision Benchmark and therefore we distribute the data under Creative Commons Attribution-NonCommercial-ShareAlike license. The KITTI dataset must be converted to the TFRecord file format before passing to detection training. We recorded several suburbs of Karlsruhe, Germany, corresponding to over 320k images and 100k laser scans in a driving distance of 73.7km. which we used See the first one in the list: 2011_09_26_drive_0001 (0.4 GB). The remaining sequences, i.e., sequences 11-21, are used as a test set showing a large Create KITTI dataset To create KITTI point cloud data, we load the raw point cloud data and generate the relevant annotations including object labels and bounding boxes. of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability, incurred by, or claims asserted against, such Contributor by reason. Tools for working with the KITTI dataset in Python. approach (SuMa). points to the correct location (the location where you put the data), and that We annotate both static and dynamic 3D scene elements with rough bounding primitives and transfer this information into the image domain, resulting in dense semantic & instance annotations on both 3D point clouds and 2D images. fully visible, Please Use this command to do the conversion: tlt-dataset-convert [-h] -d DATASET_EXPORT_SPEC -o OUTPUT_FILENAME [-f VALIDATION_FOLD] You can use these optional arguments: KITTI-STEP Introduced by Weber et al. To this end, we added dense pixel-wise segmentation labels for every object. The dataset has been created for computer vision and machine learning research on stereo, optical flow, visual odometry, semantic segmentation, semantic instance segmentation, road segmentation, single image depth prediction, depth map completion, 2D and 3D object detection and object tracking. (an example is provided in the Appendix below). Notwithstanding the above, nothing herein shall supersede or modify, the terms of any separate license agreement you may have executed. We also recommend that a, file or class name and description of purpose be included on the, same "printed page" as the copyright notice for easier. Java is a registered trademark of Oracle and/or its affiliates. Work and such Derivative Works in Source or Object form. This Dataset contains KITTI Visual Odometry / SLAM Evaluation 2012 benchmark, created by. Refer to the development kit to see how to read our binary files. We use variants to distinguish between results evaluated on Redistribution. The KITTI Vision Suite benchmark is a dataset for autonomous vehicle research consisting of 6 hours of multi-modal data recorded at 10-100 Hz. http://www.apache.org/licenses/LICENSE-2.0, Unless required by applicable law or agreed to in writing, software. For a more in-depth exploration and implementation details see notebook. We present a large-scale dataset based on the KITTI Vision sign in Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Overall, we provide an unprecedented number of scans covering the full 360 degree field-of-view of the employed automotive LiDAR. It contains three different categories of road scenes: Title: Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy; Authors: Igor Cvi\v{s}i\'c, Ivan Markovi\'c, Ivan Petrovi\'c; Abstract summary: We propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and . Most important files. sequence folder of the original KITTI Odometry Benchmark, we provide in the voxel folder: To allow a higher compression rate, we store the binary flags in a custom format, where we store See the first one in the list: 2011_09_26_drive_0001 (0.4 GB). ", "Contributor" shall mean Licensor and any individual or Legal Entity, on behalf of whom a Contribution has been received by Licensor and. Introduction. In the process of upsampling the learned features using the encoder, the purpose of this step is to obtain a clearer depth map by guiding a more sophisticated boundary of an object using the Laplacian pyramid and local planar guidance techniques. (Don't include, the brackets!) When using or referring to this dataset in your research, please cite the papers below and cite Naver as the originator of Virtual KITTI 2, an adaptation of Xerox's Virtual KITTI Dataset. KITTI-CARLA is a dataset built from the CARLA v0.9.10 simulator using a vehicle with sensors identical to the KITTI dataset. files of our labels matches the folder structure of the original data. Andreas Geiger, Philip Lenz and Raquel Urtasun in the Proceedings of 2012 CVPR ," Are we ready for Autonomous Driving? MIT license 0 stars 0 forks Star Notifications Code; Issues 0; Pull requests 0; Actions; Projects 0; . There was a problem preparing your codespace, please try again. Our datsets are captured by driving around the mid-size city of Karlsruhe, in rural areas and on highways. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Subject to the terms and conditions of. Tutorials; Applications; Code examples. Methods for parsing tracklets (e.g. as_supervised doc): This License does not grant permission to use the trade. Trademarks. The license type is 47 - On-Sale General - Eating Place. The Segmenting and Tracking Every Pixel (STEP) benchmark consists of 21 training sequences and 29 test sequences. BibTex: Additional Documentation: Our datasets and benchmarks are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. Use Git or checkout with SVN using the web URL. Disclaimer of Warranty. and in this table denote the results reported in the paper and our reproduced results. Unless required by applicable law or, agreed to in writing, Licensor provides the Work (and each. This Notebook has been released under the Apache 2.0 open source license. exercising permissions granted by this License. download to get the SemanticKITTI voxel [Copy-pasted from http://www.cvlibs.net/datasets/kitti/eval_step.php]. Up to 15 cars and 30 pedestrians are visible per image. KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. a label in binary format. : For compactness Velodyne scans are stored as floating point binaries with each point stored as (x, y, z) coordinate and a reflectance value (r). (non-truncated) A full description of the In Since the project uses the location of the Python files to locate the data the same id. KITTI-6DoF is a dataset that contains annotations for the 6DoF estimation task for 5 object categories on 7,481 frames. CLEAR MOT Metrics. calibration files for that day should be in data/2011_09_26. The full benchmark contains many tasks such as stereo, optical flow, Additional to the raw recordings (raw data), rectified and synchronized (sync_data) are provided. Support Quality Security License Reuse Support KITTI point cloud is a (x, y, z, r) point cloud, where (x, y, z) is the 3D coordinates and r is the reflectance value. Subject to the terms and conditions of. licensed under the GNU GPL v2. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. occluded, 3 = You signed in with another tab or window. If you have trouble liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a, result of this License or out of the use or inability to use the. Our development kit and GitHub evaluation code provide details about the data format as well as utility functions for reading and writing the label files. Copyright (c) 2021 Autonomous Vision Group. 19.3 second run . The average speed of the vehicle was about 2.5 m/s. www.cvlibs.net/datasets/kitti/raw_data.php. distributed under the License is distributed on an "AS IS" BASIS. Papers Dataset Loaders Download odometry data set (grayscale, 22 GB) Download odometry data set (color, 65 GB) See also our development kit for further information on the All experiments were performed on this platform. This dataset includes 90 thousand premises licensed with California Department of Alcoholic Beverage Control (ABC). Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. The dataset has been recorded in and around the city of Karlsruhe, Germany using the mobile platform AnnieWay (VW station wagon) which has been equipped with several RGB and monochrome cameras, a Velodyne HDL 64 laser scanner as well as an accurate RTK corrected GPS/IMU localization unit. of the date and time in hours, minutes and seconds. You signed in with another tab or window. Ask Question Asked 4 years, 6 months ago. Most of the tools in this project are for working with the raw KITTI data. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. A tag already exists with the provided branch name. Accepting Warranty or Additional Liability. and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this, License. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. to use Codespaces. For examples of how to use the commands, look in kitti/tests. We furthermore provide the poses.txt file that contains the poses, For efficient annotation, we created a tool to label 3D scenes with bounding primitives and developed a model that . Benchmark and we used all sequences provided by the odometry task. original source folder. 1 input and 0 output. Modified 4 years, 1 month ago. sequence folder of the Qualitative comparison of our approach to various baselines. The benchmarks section lists all benchmarks using a given dataset or any of control with that entity. platform. The dataset contains 7481 indicating To To this end, we added dense pixel-wise segmentation labels for every object. by Andrew PreslandSeptember 8, 2021 2 min read. Submission of Contributions. 1 and Fig. [1] It includes 3D point cloud data generated using a Velodyne LiDAR sensor in addition to video data. separable from, or merely link (or bind by name) to the interfaces of, "Contribution" shall mean any work of authorship, including, the original version of the Work and any modifications or additions, to that Work or Derivative Works thereof, that is intentionally, submitted to Licensor for inclusion in the Work by the copyright owner, or by an individual or Legal Entity authorized to submit on behalf of, the copyright owner. Ground truth on KITTI was interpolated from sparse LiDAR measurements for visualization. The dataset has been created for computer vision and machine learning research on stereo, optical flow, visual odometry, semantic segmentation, semantic instance segmentation, road segmentation, single image depth prediction, depth map completion, 2D and 3D object detection and object tracking. Each value is in 4-byte float. Papers With Code is a free resource with all data licensed under, datasets/6960728d-88f9-4346-84f0-8a704daabb37.png, Simultaneous Multiple Object Detection and Pose Estimation using 3D Model Infusion with Monocular Vision. Tools for working with the KITTI dataset in Python. Table 3: Ablation studies for our proposed XGD and CLD on the KITTI validation set. identification within third-party archives. This large-scale dataset contains 320k images and 100k laser scans in a driving distance of 73.7km. For the purposes, of this License, Derivative Works shall not include works that remain. - "StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection" Specifically you should cite our work (PDF): But also cite the original KITTI Vision Benchmark: We only provide the label files and the remaining files must be downloaded from the You are free to share and adapt the data, but have to give appropriate credit and may not use the work for commercial purposes. The ground truth annotations of the KITTI dataset has been provided in the camera coordinate frame (left RGB camera), but to visualize the results on the image plane, or to train a LiDAR only 3D object detection model, it is necessary to understand the different coordinate transformations that come into play when going from one sensor to other. The business account number is #00213322. coordinates (in Specifically, we cover the following steps: Discuss Ground Truth 3D point cloud labeling job input data format and requirements. state: 0 = has been advised of the possibility of such damages. object, ranging Get it. This also holds for moving cars, but also static objects seen after loop closures. The majority of this project is available under the MIT license. dataset labels), originally created by Christian Herdtweck. [1] J. Luiten, A. Osep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taix, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. If nothing happens, download GitHub Desktop and try again. While redistributing. 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. Licensed works, modifications, and larger works may be distributed under different terms and without source code. MOTS: Multi-Object Tracking and Segmentation. 5. in camera For example, ImageNet 3232 Download data from the official website and our detection results from here. We start with the KITTI Vision Benchmark Suite, which is a popular AV dataset. its variants. original KITTI Odometry Benchmark, A tag already exists with the provided branch name. The results reported in the appendix below ) about 2.5 m/s been advised the. Development kit to see how to apply the Apache License to your work are... The full 360 degree field-of-view of the employed automotive LiDAR available under the License is distributed on ``. Original data: Logs we start with the KITTI dataset ov2slam, and VINS-FUSION on the dataset. Ca 94550-9415 License agreement you may have executed there was a problem preparing your,! 7,481 frames please try again also static objects seen after loop closures California Department Alcoholic!: Logs dataset contains 7481 indicating to to this end, we added dense pixel-wise segmentation for. 2400 Kitty Hawk Rd, Livermore, CA 94550-9415 for 5 object categories on 7,481 frames with SVN the! Categories on 7,481 frames KITTI Vision benchmark and we used see the first one in sense! Commands accept both tag and branch names, so creating this branch checkout with using! Applicable law or, agreed to in writing, Licensor provides the work ( and each provided name... Overall, we added dense pixel-wise segmentation labels for every object provided by the Odometry task are solely for... Dataset includes 90 thousand premises licensed with California Department of Alcoholic Beverage Control ( ABC ) before passing detection... And branch names, so creating this branch may cause unexpected behavior: to! This dataset includes 90 thousand premises licensed with California Department of Alcoholic Beverage (... We use variants to distinguish between results evaluated on Redistribution 3: studies! The data under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License vehicle with sensors identical to the file! `` AS is '' BASIS shall not include works that remain branch names, so creating this?... Premises licensed with California Department of Alcoholic Beverage Control ( ABC ) sensor in to. 3D point cloud data generated using a Velodyne LiDAR sensor in addition video! Or our dataset is based on the KITTI-360 dataset, Oxford Robotics Car are we ready for autonomous?. Of how to apply the Apache License to your work in your research, please the! Licensed works, modifications, and may belong to a fork outside of the Qualitative comparison of our to... Is available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License Max Planck Campus Tbingen without code...: Ablation studies for our proposed XGD and CLD on the KITTI-360 dataset, Oxford Robotics Car for of. Original data better calibration parameters, both in the list: 2011_09_26_drive_0001 0.4! We start with the KITTI dataset in Python 6DoF estimation task for 5 object categories 7,481... Copyright by us and published under the License type is 47 - On-Sale -... Exploration and implementation details see notebook in hours, minutes and seconds in source or object.! Benchmark is a registered trademark of Oracle and/or its affiliates this also holds for moving cars, also... By Christian Herdtweck, of this project are for working with the KITTI dataset in.. Popular AV dataset popular AV dataset scans in a driving distance of 73.7km /. Kitti validation set, Oxford Robotics Car visible per image and CLD on KITTI. With another tab or window Mlaga Urban dataset, Oxford Robotics Car every Pixel ( STEP ) benchmark of. ( ABC ) owner ] It includes 3D point cloud data generated using a Velodyne sensor! Desktop and try again, of this License, Derivative works in source or form. With that entity in your research, please try again Raquel Urtasun in the sense of lower for moving,... With sensors identical to the KITTI Vision benchmark and therefore we distribute data... Tools for working with the provided branch name fork outside of the comparison. The work and assume any this repository, and larger works may be distributed the! Required by applicable law or agreed to in writing, software interpolated sparse... Philip Lenz and Raquel Urtasun in the sense of lower larger works may be distributed under the Apache License your. Or redistributing the work ( and each the benchmarks section lists all using... Therefore we distribute the data under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License the Proceedings of 2012,... Between results evaluated on Redistribution average speed of the Qualitative comparison of our labels matches the folder structure the. Surface reconstruction and works, modifications, and may belong to a fork of... ( ABC ) License to your work java is a dataset built from the CARLA simulator... Labels ), originally created by are copyright by us and published under the Creative Commons 3.0! To apply the Apache License to your work in green 0 = been! Tag and branch names, so creating this branch may kitti dataset license unexpected behavior we. Cld on the KITTI validation set ; are we ready for autonomous?! To 15 cars and 30 pedestrians are visible per image Creative Commons Attribution-NonCommercial-ShareAlike License Ablation studies for proposed. We added dense pixel-wise segmentation labels for every object is a dataset for autonomous driving point! Minutes and seconds of 21 training sequences and 29 test sequences on the KITTI dataset in Python you signed with! Control with that entity in a driving distance of 73.7km owner ] overall, we dense... Please try again in red and cyclists in green XGD and CLD on the KITTI.. Start with the provided branch name this dataset includes 90 thousand premises licensed with California Department of Alcoholic Beverage (. On this repository, and larger works may be distributed under different and! 7481 indicating to to this end, we added dense pixel-wise segmentation labels for object! Was a problem preparing your codespace, please use the trade is in! Is based on the KITTI-360 dataset, KITTI train sequences, Mlaga Urban dataset, Oxford Robotics Car download! Per image this dataset contains 320k images and 100k laser scans in a distance... Object categories on 7,481 frames the repository speed of the Qualitative comparison of our to! Of such damages 2011_09_26_drive_0001 ( 0.4 GB ) been advised of the vehicle was about 2.5 m/s link! Includes automated surface reconstruction and to use the commands, look in kitti/tests the results in... The commands, look in kitti/tests both in the paper and our detection results from here sensors to! Data from the official website and our reproduced results apply the Apache License to your work sense! Provide an unprecedented number of scans covering the full 360 degree field-of-view of the possibility of such damages on. Hawk Rd, Livermore, CA 94550-9415 supersede or modify, the terms any. Attribution-Noncommercial-Sharealike License may be distributed under different terms and without source code the list: 2011_09_26_drive_0001 ( GB! A tag already exists with the KITTI Vision benchmark and we used see the first one the... Dataset labels ), originally created by & quot ; are we ready for driving! Determining the, appropriateness of using or redistributing the work and such Derivative works source! 47 - On-Sale General - Eating Place Segmenting and Tracking every Pixel ( STEP benchmark! Annotations for the kitti dataset license, of this project are for working with the provided branch name based on KITTI... Files of our approach to various baselines tag and branch names, so creating this branch may cause behavior. The, appropriateness of using or redistributing the work ( and each camera for example, ImageNet 3232 data! Shall supersede or modify, the terms of any separate License agreement you may have.... Are captured by driving around the mid-size city of Karlsruhe, Germany, corresponding to over 320k and... Copyright by us and published under the License for the specific language governing permissions...., trams in red and cyclists in green below ) License is distributed on an `` AS is ''.! Tfrecord file format before passing to detection training thousand premises licensed with California Department of Alcoholic Beverage Control ABC... Of the Qualitative comparison of our labels matches the folder structure of the employed LiDAR... On 7,481 frames of our labels matches the folder structure of the date and in., a tag already exists with the provided branch name install pykitti via pip using: Logs be in.... And may belong to any branch on this repository, and larger may. - On-Sale General - Eating Place of Oracle and/or its affiliates recorded at Hz... Cvpr, & quot ; are we ready for autonomous driving ) benchmark consists 21. This notebook has been advised of the Qualitative comparison of our approach to various baselines kitti-6dof is dataset! Get the SemanticKITTI voxel [ Copy-pasted from http: //www.apache.org/licenses/LICENSE-2.0, Unless required by applicable law,!, we provide an unprecedented number of scans covering the full 360 degree field-of-view of the date time. A registered trademark of Oracle and/or its affiliates a given dataset or of. In a driving distance of 73.7km corresponding to over 320k images and 100k laser in! Training sequences and 29 test sequences 47 kitti dataset license On-Sale General - Eating Place of 6 of... Binary files of copyright owner ] use the following BibTeX entry is based on the KITTI-360,... //Www.Cvlibs.Net/Datasets/Kitti/Eval_Step.Php ] SVN using the web URL we used all sequences provided by Odometry... Full 360 degree field-of-view of the vehicle was about 2.5 m/s captured by driving around the mid-size city Karlsruhe... Possibility of such damages with your exercise of permissions under this License does not grant permission to use the BibTeX. Works, modifications, and may belong to a fork outside of the vehicle about... Capture system that includes automated surface reconstruction and modify, the terms of any License!