DE top-level domain. amazing list of colors!. 1 Performance evaluation on TUM RGB-D dataset The TUM RGB-D dataset was proposed by the TUM Computer Vision Group in 2012, which is frequently used in the SLAM domain [ 6 ]. The categorization differentiates. tum. The following seven sequences used in this analysis depict different situations and intended to test robustness of algorithms in these conditions. In this section, our method is tested on the TUM RGB-D dataset (Sturm et al. The TUM dataset is divided into high-dynamic datasets and low-dynamic datasets. rbg. It takes a few minutes with ~5G GPU memory. To obtain poses for the sequences, we run the publicly available version of Direct Sparse Odometry. The TUM RGBD dataset [10] is a large set of data with sequences containing both RGB-D data and ground truth pose estimates from a motion capture system. tum. These tasks are being resolved by one Simultaneous Localization and Mapping module called SLAM. Evaluation of Localization and Mapping Evaluation on Replica. tum. The Private Enterprise Number officially assigned to Technische Universität München by the Internet Assigned Numbers Authority (IANA) is: 19518. TUM RGB-D. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. , Monodepth2. Previously, I worked on fusing RGB-D data into 3D scene representations in real-time and improving the quality of such reconstructions with various deep learning approaches. unicorn. de and the Knowledge Database kb. Live-RBG-Recorder. This may be due to: You've not accessed this login-page via the page you wanted to log in (eg. rbg. while in the challenging TUM RGB-D dataset, we use 30 iterations for tracking, with max keyframe interval µ k = 5. Our abuse contact API returns data containing information. 0. The video shows an evaluation of PL-SLAM and the new initialization strategy on a TUM RGB-D benchmark sequence. RELATED WORK A. The benchmark website contains the dataset, evaluation tools and additional information. manhardt, nassir. r. This repository is linked to the google site. TUM rgb-d data set contains rgb-d image. mine which regions are static and dynamic relies only on anIt can effectively improve robustness and accuracy in dynamic indoor environments. Both groups of sequences have important challenges such as missing depth data caused by sensor range limit. However, these DATMO. , chairs, books, and laptops) can be used by their VSLAM system to build a semantic map of the surrounding. The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). First, both depths are related by a deformation that depends on the image content. Many answers for common questions can be found quickly in those articles. deIm Beschaffungswesen stellt die RBG die vergaberechtskonforme Beschaffung von Hardware und Software sicher und etabliert und betreut TUM-weite Rahmenverträge und. bash scripts/download_tum. In order to verify the preference of our proposed SLAM system, we conduct the experiments on the TUM RGB-D datasets. de; Exercises: individual tutor groups (Registration required. The actions can be generally divided into three categories: 40 daily actions (e. net. de(PTR record of primary IP) IPv4: 131. github","contentType":"directory"},{"name":". [email protected] is able to detect loops and relocalize the camera in real time. Compared with ORB-SLAM2, the proposed SOF-SLAM achieves averagely 96. Tumbler Ridge is a district municipality in the foothills of the B. The standard training and test set contain 795 and 654 images, respectively. The stereo case shows the final trajectory and sparse reconstruction of the sequence 00 from the KITTI dataset [2]. TUM-Live, the livestreaming and VoD service of the Rechnerbetriebsgruppe at the department of informatics and mathematics at the Technical University of MunichIn the experiment, the mainstream public dataset TUM RGB-D was used to evaluate the performance of the SLAM algorithm proposed in this paper. in. tum. News DynaSLAM supports now both OpenCV 2. We select images in dynamic scenes for testing. The TUM RGBD dataset [10] is a large set of data with sequences containing both RGB-D data and ground truth pose estimates from a motion capture system. No direct hits Nothing is hosted on this IP. 756098 Experimental results on the TUM dynamic dataset show that the proposed algorithm significantly improves the positioning accuracy and stability for the datasets with high dynamic environments, and is a slight improvement for the datasets with low dynamic environments compared with the original DS-SLAM algorithm. AS209335 - TUM-RBG, DE Note: An IP might be announced by multiple ASs. Tutorial 02 - Math Recap Thursday, 10/27/2022, 04:00 AM. Run. ORB-SLAM2. tum. The proposed DT-SLAM approach is validated using the TUM RBG-D and EuRoC benchmark datasets for location tracking performances. Livestream on Artemis → Lectures or live. This repository is the collection of SLAM-related datasets. The TUM RGB-D dataset , which includes 39 sequences of offices, was selected as the indoor dataset to test the SVG-Loop algorithm. The calibration of the RGB camera is the following: fx = 542. /Datasets/Demo folder. de email address. This file contains information about publicly available datasets suited for monocular, stereo, RGB-D and lidar SLAM. We provide examples to run the SLAM system in the KITTI dataset as stereo or monocular, in the TUM dataset as RGB-D or monocular, and in the EuRoC dataset as stereo or monocular. The sequences include RGB images, depth images, and ground truth trajectories. txt is provided for compatibility with the TUM RGB-D benchmark. Visual Odometry is an important area of information fusion in which the central aim is to estimate the pose of a robot using data collected by visual sensors. TUM RGB-D Dataset and Benchmark. [NYUDv2] The NYU-Depth V2 dataset consists of 1449 RGB-D images showing interior scenes, which all labels are usually mapped to 40 classes. Tumexam. The button save_traj saves the trajectory in one of two formats (euroc_fmt or tum_rgbd_fmt). 04. We also provide a ROS node to process live monocular, stereo or RGB-D streams. Qualitative and quantitative experiments show that our method outperforms state-of-the-art approaches in various dynamic scenes in terms of both accuracy and robustness. TUM RBG-D can be used with TUM RGB-D or UZH trajectory evaluation tools and has the following format timestamp[s] tx ty tz qx qy qz qw. 38: AS4837: CHINA169-BACKBONE CHINA. Seen 7 times between July 18th, 2023 and July 18th, 2023. Mystic Light. Many also prefer TKL and 60% keyboards for the shorter 'throw' distance to the mouse. ORG top-level domain. 16% green and 43. The freiburg3 series are commonly used to evaluate the performance. The experiments on the public TUM dataset show that, compared with ORB-SLAM2, the MOR-SLAM improves the absolute trajectory accuracy by 95. We select images in dynamic scenes for testing. Cremers LSD-SLAM: Large-Scale Direct Monocular SLAM European Conference on Computer Vision (ECCV), 2014. Features include: Automatic lecture scheduling and access management coupled with CAMPUSOnline. Last update: 2021/02/04. However, most visual SLAM systems rely on the static scene assumption and consequently have severely reduced accuracy and robustness in dynamic scenes. tum. , 2012). It is a challenging dataset due to the presence of. Then Section 3 includes experimental comparison with the original ORB-SLAM2 algorithm on TUM RGB-D dataset (Sturm et al. net. Content. Compared with art-of-the-state methods, experiments on the TUM RBG-D dataset, KITTI odometry dataset, and practical environment show that SVG-Loop has advantages in complex environments with varying light, changeable weather, and dynamic interference. g. 15. 2. Lecture 1: Introduction Tuesday, 10/18/2022, 05:00 AM. Visual odometry and SLAM datasets: The TUM RGB-D dataset [14] is focused on the evaluation of RGB-D odometry and SLAM algorithms and has been extensively used by the research community. 2023. We conduct experiments both on TUM RGB-D dataset and in real-world environment. The dataset has RGB-D sequences with ground truth camera trajectories. md","path":"README. For those already familiar with RGB control software, it may feel a tad limiting and boring. Sie finden zudem eine. TUM RGB-D contains the color and depth images of real trajectories and provides acceleration data from a Kinect sensor. tum. 基于RGB-D 的视觉SLAM（同时定位与建图）算法基本都假设环境是静态的，然而在实际环境中经常会出现动态物体，导致SLAM 算法性能的下降．为此. Two consecutive key frames usually involve sufficient visual change. Telephone: 089 289 18018. See the settings file provided for the TUM RGB-D cameras. Definition, Synonyms, Translations of TBG by The Free DictionaryBlack Bear in the Victoria harbourVPN-Connection to the TUM set up of the RBG certificate Furthermore the helpdesk maintains two websites. Registrar: RIPENCC Route: 131. 593520 cy = 237. 19 IPv6: 2a09:80c0:92::19: Live Screenshot Hover to expand. The predicted poses will then be optimized by merging. Many answers for common questions can be found quickly in those articles. We conduct experiments both on TUM RGB-D dataset and in the real-world environment. The ground-truth trajectory is obtained from a high-accuracy motion-capture system. Attention: This is a live snapshot of this website, we do not host or control it! No direct hits. Both groups of sequences have important challenges such as missing depth data caused by sensor. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. The LCD screen on the remote clearly shows the. The TUM RGB-D dataset’s indoor instances were used to test their methodology, and they were able to provide results that were on par with those of well-known VSLAM methods. TUM RGB-D Benchmark Dataset [11] is a large dataset containing RGB-D data and ground-truth camera poses. ManhattanSLAM. It contains indoor sequences from RGB-D sensors grouped in several categories by different texture, illumination and structure conditions. org traffic statisticsLog-in. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. RBG – Rechnerbetriebsgruppe Mathematik und Informatik Helpdesk: Montag bis Freitag 08:00 - 18:00 Uhr Telefon: 18018 Mail: [email protected]. We select images in dynamic scenes for testing. tum. Registrar: RIPENCC. de TUM-Live. However, loop closure based on 3D points is more simplistic than the methods based on point features. tum. RGB-D dataset and benchmark for visual SLAM evaluation: Rolling-Shutter Dataset: SLAM for Omnidirectional Cameras: TUM Large-Scale Indoor (TUM LSI) Dataset:ORB-SLAM2的编译运行以及TUM数据集测试. Our experimental results have showed the proposed SLAM system outperforms the ORB. Among various SLAM datasets, we've selected the datasets provide pose and map information. We provide the time-stamped color and depth images as a gzipped tar file (TGZ). The results indicate that DS-SLAM outperforms ORB-SLAM2 significantly regarding accuracy and robustness in dynamic environments. To observe the influence of the depth unstable regions on the point cloud, we utilize a set of RGB and depth images selected form TUM dataset to obtain the local point cloud, as shown in Fig. We recommend that you use the 'xyz' series for your first experiments. Tickets: [email protected]. Information Technology Technical University of Munich Arcisstr. The ground-truth trajectory was Dataset Download. The calibration of the RGB camera is the following: fx = 542. We provide examples to run the SLAM system in the KITTI dataset as stereo or monocular, in the TUM dataset as RGB-D or monocular, and in the EuRoC dataset as stereo or monocular. You can change between the SLAM and Localization mode using the GUI of the map. 3 are now supported. The RGB and depth images were recorded at frame rate of 30 Hz and a 640 × 480 resolution. Tracking Enhanced ORB-SLAM2. stereo, event-based, omnidirectional, and Red Green Blue-Depth (RGB-D) cameras. If you want to contribute, please create a pull request and just wait for it to be. Seen 143 times between April 1st, 2023 and April 1st, 2023. de. This table can be used to choose a color in WebPreferences of each web. These sequences are separated into two categories: low-dynamic scenarios and high-dynamic scenarios. The stereo case shows the final trajectory and sparse reconstruction of the sequence 00 from the KITTI dataset [2]. tum. Experiments on public TUM RGB-D dataset and in real-world environment are conducted. Change your RBG-Credentials. The following seven sequences used in this analysis depict different situations and intended to test robustness of algorithms in these conditions. RGB and HEX color codes of TUM colors. The results indicate that the proposed DT-SLAM (mean RMSE = 0:0807. RGB and HEX color codes of TUM colors. tum. : You need VPN ( VPN Chair) to open the Qpilot Website. sh","path":"_download. Share study experience about Computer Vision, SLAM, Deep Learning, Machine Learning, and RoboticsRGB-live . Contribution. In the end, we conducted a large number of evaluation experiments on multiple RGB-D SLAM systems, and analyzed their advantages and disadvantages, as well as performance differences in different. Freiburg3 consists of a high-dynamic scene sequence marked 'walking', in which two people walk around a table, and a low-dynamic scene sequence marked 'sitting', in which two people sit in chairs with slight head or part. Attention: This is a live. This repository is a fork from ORB-SLAM3. The video sequences are recorded by an RGB-D camera from Microsoft Kinect at a frame rate of 30 Hz, with a resolution of 640 × 480 pixel. 5. 1. ORB-SLAM2是一套完整的SLAM方案，提供了单目，双目和RGB-D三种接口。. We have four papers accepted to ICCV 2023. Thumbnail Figures from Complex Urban, NCLT, Oxford robotcar, KiTTi, Cityscapes datasets. It contains indoor sequences from RGB-D sensors grouped in several categories by different texture, illumination and structure conditions. TUM-Live, the livestreaming and VoD service of the Rechnerbetriebsgruppe at the department of informatics and mathematics at the Technical University of Munich{"payload":{"allShortcutsEnabled":false,"fileTree":{"Examples/RGB-D":{"items":[{"name":"associations","path":"Examples/RGB-D/associations","contentType":"directory. g. de / rbg@ma. In the RGB color model #34526f is comprised of 20. however, the code for the orichid color is E6A8D7, not C0448F as it says, since it already belongs to red violet. 17123 [email protected] human stomach or abdomen. However, there are many dynamic objects in actual environments, which reduce the accuracy and robustness of. This is not shown. This is an urban sequence with multiple loop closures that ORB-SLAM2 was able to successfully detect. $ . VPN-Connection to the TUM set up of the RBG certificate Furthermore the helpdesk maintains two websites. 80% / TKL Keyboards (Tenkeyless) As the name suggests, tenkeyless mechanical keyboards are essentially standard full-sized keyboards without a tenkey / numberpad. The Wiki wiki. vmcarle35. TUM-Live, the livestreaming and VoD service of the Rechnerbetriebsgruppe at the department of informatics and mathematics at the Technical University of Munich. Furthermore, it has acceptable level of computational. +49. g. This is not shown. It includes 39 indoor scene sequences, of which we selected dynamic sequences to evaluate our system. Students have an ITO account and have bought quota from the Fachschaft. Furthermore, the KITTI dataset. Year: 2009; Publication: The New College Vision and Laser Data Set; Available sensors: GPS, odometry, stereo cameras, omnidirectional camera, lidar; Ground truth: No The TUM RGB-D dataset [39] con-tains sequences of indoor videos under different environ-ment conditions e. two example RGB frames from a dynamic scene and the resulting model built by our approach. Ground-truth trajectories obtained from a high-accuracy motion-capture system are provided in the TUM datasets. ntp1 und ntp2 sind Stratum 3 Server. Follow us on: News. , illuminance and varied scene settings, which include both static and moving object. Telefon: 18018. Major Features include a modern UI with dark-mode Support and a Live-Chat. אוניברסיטה בגרמניהDRG-SLAM is presented, which combines line features and plane features into point features to improve the robustness of the system and has superior accuracy and robustness in indoor dynamic scenes compared with the state-of-the-art methods. 4. in. Each file is listed on a separate line, which is formatted like: timestamp file_path RGB-D data. It not only can be used to scan high-quality 3D models, but also can satisfy the demand. We are capable of detecting the blur and removing blur interference. We provided an. This is not shown. de credentials) Kontakt Rechnerbetriebsgruppe der Fakultäten Mathematik und Informatik Telefon: 18018. from publication: Evaluating Egomotion and Structure-from-Motion Approaches Using the TUM RGB-D Benchmark. ple datasets: TUM RGB-D dataset [14] and Augmented ICL-NUIM [4]. the initializer is very slow, and does not work very reliably. It is able to detect loops and relocalize the camera in real time. g. The TUM RGB-D dataset provides many sequences in dynamic indoor scenes with accurate ground-truth data. We evaluated ReFusion on the TUM RGB-D dataset [17], as well as on our own dataset, showing the versatility and robustness of our approach, reaching in several scenes equal or better performance than other dense SLAM approaches. . de. vmknoll42. der Fakultäten. Our dataset contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. in. and TUM RGB-D [42], our framework is shown to outperform both monocular SLAM system (i. The persons move in the environments. The TUM Corona Crisis Task Force ([email protected]. Uh oh!. tum. It contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. Compared with art-of-the-state methods, experiments on the TUM RBG-D dataset, KITTI odometry dataset, and practical environment show that SVG-Loop has advantages in complex environments with. The sensor of this dataset is a handheld Kinect RGB-D camera with a resolution of 640 × 480. of the. Available for: Windows. idea. The second part is in the TUM RGB-D dataset, which is a benchmark dataset for dynamic SLAM. The sequences contain both the color and depth images in full sensor resolution (640 × 480). RBG. 89. First, download the demo data as below and the data is saved into the . 1. 5. In case you need Matlab for research or teaching purposes, please contact support@ito. tum- / RBG-account is entirely seperate form the LRZ- / TUM-credentials. However, the method of handling outliers in actual data directly affects the accuracy of. It offers RGB images and depth data and is suitable for indoor environments. 5. the Xerox-Printers. Large-scale experiments are conducted on the ScanNet dataset, showing that volumetric methods with our geometry integration mechanism outperform state-of-the-art methods quantitatively as well as qualitatively. md","contentType":"file"},{"name":"_download. 15th European Conference on Computer Vision, September 8 – 14, 2018 | Eccv2018 - Eccv2018. Per default, dso_dataset writes all keyframe poses to a file result. 1 On blackboxes in Rechnerhalle; 1. Gnunet. The Dynamic Objects sequences in TUM dataset are used in order to evaluate the performance of SLAM systems in dynamic environments. Full size table. 822841 fy = 542. Joan Ruth Bader Ginsburg ( / ˈbeɪdər ˈɡɪnzbɜːrɡ / BAY-dər GHINZ-burg; March 15, 1933 – September 18, 2020) [1] was an American lawyer and jurist who served as an associate justice of the Supreme Court of the United States from 1993 until her death in 2020. 289. The sequences include RGB images, depth images, and ground truth trajectories. Link to Dataset. Two different scenes (the living room and the office room scene) are provided with ground truth. Totally Accurate Battlegrounds (TABG) is a parody of the Battle Royale genre. However, this method takes a long time to calculate, and its real-time performance is difficult to meet people's needs. The TUM RGB-D benchmark [5] consists of 39 sequences that we recorded in two different indoor environments. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". 822841 fy = 542. The reconstructed scene for fr3/walking-halfsphere from the TUM RBG-D dynamic dataset. 2. 非线性因子恢复的视觉惯性建图。Mirror of the Basalt repository. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). TUM-Live, the livestreaming and VoD service of the Rechnerbetriebsgruppe at the department of informatics and mathematics at the Technical University of MunichKey Frames: A subset of video frames that contain cues for localization and tracking. 1illustrates the tracking performance of our method and the state-of-the-art methods on the Replica dataset. de TUM RGB-D is an RGB-D dataset. This zone conveys a joint 2D and 3D information corresponding to the distance of a given pixel to the nearest human body and the depth distance to the nearest human, respectively. Sie finden zudem eine Zusammenfassung der wichtigsten Informationen für neue Benutzer auch in unserem. net server is located in Switzerland, therefore, we cannot identify the countries where the traffic is originated and if the distance can potentially affect the page load time. Yayınlandığı dönemde milyonlarca insanın kalbine taht kuran ve zengin kız ile fakir erkeğin aşkını anlatan Meri Aashiqui Tum Se Hi, ‘Kara Sevdam’ adıyla YouT. In these datasets, Dynamic Objects contains nine datasetsAS209335 - TUM-RBG, DE Note: An IP might be announced by multiple ASs. 89. 0/16 (Route of ASN) PTR: unicorn. , drinking, eating, reading), nine health-related actions (e. 159. Moreover, the metric. © RBG Rechnerbetriebsgruppe Informatik, Technische Universität München, 2013–2018, [email protected] guide The RBG Helpdesk can support you in setting up your VPN. Tickets: rbg@in. Ground-truth trajectories obtained from a high-accuracy motion-capture system are provided in the TUM datasets. The sequences are from TUM RGB-D dataset. © RBG Rechnerbetriebsgruppe Informatik, Technische Universität München, 2013–2018, [email protected]. We show. Monocular SLAM PTAM [18] is a monocular, keyframe-based SLAM system which was the first work to introduce the idea of splitting camera tracking and mapping into parallel threads, and. The dynamic objects have been segmented and removed in these synthetic images. 德国慕尼黑工业大学TUM计算机视觉组2012年提出了一个RGB-D数据集，是目前应用最为广泛的RGB-D数据集。数据集使用Kinect采集，包含了depth图像和rgb图像，以及ground truth等数据，具体格式请查看官网。on the TUM RGB-D dataset. Login (with in. github","path":". Synthetic RGB-D dataset. TUM Mono-VO. DE zone. Diese sind untereinander und mit zwei weiteren Stratum 2 Zeitservern (auch bei der RBG gehostet) in einem Peerverband. Living room has 3D surface ground truth together with the depth-maps as well as camera poses and as a result pefectly suits not just for bechmarking camera. dePrinting via the web in Qpilot. de / [email protected]. We will send an email to this address with a link to validate your new email address. 55%. Note: during the corona time you can get your RBG ID from the RBG. A novel semantic SLAM framework detecting. Similar behaviour is observed in other vSLAM [23] and VO [12] systems as well. Visual SLAM methods based on point features have achieved acceptable results in texture-rich. Once this works, you might want to try the 'desk' dataset, which covers four tables and contains several loop closures. [3] check moving consistency of feature points by epipolar constraint. Experimental results on the TUM RGB-D and the KITTI stereo datasets demonstrate our superiority over the state-of-the-art. de. The network input is the original RGB image, and the output is a segmented image containing semantic labels. de(PTR record of primary IP) IPv4: 131. Bei Fragen steht unser Helpdesk gerne zur Verfügung! RBG Helpdesk. de. Seen 1 times between June 28th, 2023 and June 28th, 2023. For interference caused by indoor moving objects, we add the improved lightweight object detection network YOLOv4-tiny to detect dynamic regions, and the dynamic features in the dynamic area are then eliminated in. 2022 from 14:00 c. 02:19:59. Living room has 3D surface ground truth together with the depth-maps as well as camera poses and as a result perfectly suits not just for benchmarking camera. The results show that the proposed method increases accuracy substantially and achieves large-scale mapping with acceptable overhead. No direct hits Nothing is hosted on this IP. Chao et al. 159. de) or your attending physician can advise you in this regard. de Welcome to the RBG user central. tum. 593520 cy = 237. de In this part, the TUM RGB-D SLAM datasets were used to evaluate the proposed RGB-D SLAM method. C. We provide examples to run the SLAM system in the KITTI dataset as stereo or. The ICL-NUIM dataset aims at benchmarking RGB-D, Visual Odometry and SLAM algorithms. 2. in. tum. Simultaneous Localization and Mapping is now widely adopted by many applications, and researchers have produced very dense literature on this topic. There are multiple configuration variants: standard - general purpose; 2. 756098Evaluation on the TUM RGB-D dataset. de. An Open3D RGBDImage is composed of two images, RGBDImage. In this paper, we present the TUM RGB-D bench-mark for visual odometry and SLAM evaluation and report on the first use-cases and users of it outside our own group. Motchallenge. tum. Therefore, a SLAM system can work normally under the static-environment assumption. de. The data was recorded at full frame rate (30 Hz) and sensor res-olution 640 480. Many answers for common questions can be found quickly in those articles. We evaluated ReFusion on the TUM RGB-D dataset [17], as well as on our own dataset, showing the versatility and robustness of our approach, reaching in several scenes equal or better performance than other dense SLAM approaches. Ultimately, Section. Next, run NICE-SLAM. Bauer Hörsaal (5602. 2. tum. RBG – Rechnerbetriebsgruppe Mathematik und Informatik Helpdesk: Montag bis Freitag 08:00 - 18:00 Uhr Telefon: 18018 Mail: rbg@in. You can create a map database file by running one of the run_****_slam executables with --map-db-out map_file_name. It is able to detect loops and relocalize the camera in real time. In particular, our group has a strong focus on direct methods, where, contrary to the classical pipeline of feature extraction and matching, we directly optimize intensity errors. There are great expectations that such systems will lead to a boost of new 3D perception-based applications in the fields of. , 2012). g. 0. 1 Comparison of experimental results in TUM data set. Authors: Raul Mur-Artal, Juan D. GitHub Gist: instantly share code, notes, and snippets.