Solibri Model Checker V7 Crack Cocaine
Sherri Herston
This website provides a list of frequently used computer vision datasets. Wait, there is more!
There is also a description containing common problems, pitfalls and characteristics and now a searchable TAG cloud.
Plus, this is open for crowd editing (if you pass the ultimate turing test)! - Questions? yacvid [at] hayko [dot] at
Content, design and idea by Hayko Riemenschneider, 2011-2024. Texts and images are subject of copyright by the respective authors.
Hey! If you're reading this, why not help and update the description of the dataset you're working on? Add a new dataset! Yay!
The CropAndWeed dataset is focused on the fine-grained identification of 74 relevant crop and weed species with a strong emphasis on data variability. Annotations of labeled bounding boxes, semantic masks and stem positions are provided for about 112k instances in more than 8k high-resolution images of both real-world agricultural sites and specifically cultivated outdoor plots of rare weed types. Additionally, each sample is enriched with meta-annotations regarding environmental conditions.
Dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional state-of-the-art computer vision methods. Nonetheless, these methods require large amounts of reliable ground-truth data. Despite their already existing several datasets that could be used for depth estimation, almost none of them are outdoor-oriented from an egocentric point of view. The dataset introduces a large number of high-definition pairs of color frames and corresponding depth maps from a human perspective. In addition, the proposed dataset also features human interaction and great variability of data.
Paper: -019-0168-5
The GRASP MultiCam data set combines recorded images from a synchronized stereo monochrome camera and IMU with those from a depth sensor. The stereo camera / IMU device allows for accurate Visual-Inertial Odometry (VIO), which can then be used to recover 3D structure from the depth sensor point clouds.
The data covers indoor and outdoor scenes. The recording devices are always carried by hand. All data is in ROS bag format.
The TrimBot2020 project researched the underlying robotics and vision technologies and prototypes the next generation of intelligent gardening consumer robots.
This dataset contains sensor data recorded from cameras and other sensors mounted on a robotic platform as well as additional external sensors capturing the robot in the garden, used for 3D Reconstruction Meets Semantics Challenge.
A multi-camera rig is mounted on top of the robot, enabling the use of both stereo and motion stereo information. Precise ground truth for the 3D structure of the garden has been obtained with a laser scanner and accurate pose estimates for the robot are available as well. Ground truth semantic labels and ground truth depth from a laser scan can be used for benchmarking the quality of the 3D reconstructions.
Detecting anomalies in videos is a complex problem with a myriad of applications in video surveillance. However, large and complex datasets that are representative of the real-world deployment of surveillance cameras are unavailable. Anomalies in surveillance videos are not well defined and the standard and existing metrics for evaluation do not quantify the performance of algorithms accurately. We provide a large scale dataset, A Day on Campus (ADOC), with 25 event types, spanning over 825 instances and occurring over a period of 24 hours. This is the largest dataset with localized bounding box annotations that is available to perform anomaly detection.
Real World Textured Things (RWTT) is a collection of publicly available textured 3D models, generated with modern off-the-shelf photo-reconstruction tools. The aim of this dataset is to provide a challenging benchmark for Geometry Processing algorithms targeted to parametrized, textured 3D models coming from the real world.
Overview
The dataset consists of 568 textured models generated with various 3D reconstruction pipelines and published on SketchFab with permissive licenses.
Metrics: Each model comes with a set of metrics reflecting the quality of the mesh geometry, its parameterization and information about the texture files. The same metrics can also be computed for user-provided models using the assessment tool TexMetro
Metadata: Model descriptions and information about the authors, tags, software used and publishing dates are also available, and the data can be searched and browsed using a web interface.
Andrea Maggiordomo, Federico Ponchio, Paolo Cignoni, Marco Tarini,
Real-World Textured Things: A repository of textured models generated with modern photo-reconstruction tools,
Computer Aided Geometric Design, Volume 83, 2020,
SUN (Showa University and Nagoya University) Colonoscopy Video Database is designed for the evaluation of an automated colorectal-polyp detection. The database comprises of approximately 160,000 still images of videos with full location-annotations and pathological data of 100 polyps, which are collected at the Showa University Northern Yokohama Hospital. Mori Laboratory, Graduate School of Informatics, Nagoya University developed this database. Every frame in the database was annotated by the expert endoscopists at Showa University. For details, please visit our website ( ).
The Replica Dataset is a dataset of high quality reconstructions of a variety of indoor spaces. Each reconstruction has clean dense geometry, high resolution and high dynamic range textures, glass and mirror surface information, planar segmentation as well as semantic class and instance segmentation. See the technical report for more details.
The Replica SDK contained in this repository allows visual inspection of the datasets via the ReplicaViewer and gives an example of how to render out images from the scenes headlessly via the ReplicaRenderer.
For machine learning purposes each dataset also contains an export to the format employed by AI Habitat and is therefore usable seamlessly in that framework for AI agent training and other ML tasks.
Animals are widespread in nature and the analysis of their shape and motion is important in many fields and industries. Modeling 3D animal shape, however, is difficult because the 3D scanning methods used to capture human shape are not applicable to wild animals or natural settings. Consequently, we propose a method to capture the detailed 3D shape of animals from images alone. The articulated and deformable nature of animals makes this problem extremely challenging, particularly in unconstrained environments with moving and uncalibrated cameras. To make this possible, we use a strong prior model of articulated animal shape that we fit to the image data. We then deform the animal shape in a canonical reference pose such that it matches image evidence when articulated and projected into multiple images. Our method extracts significantly more 3D shape detail than previous methods and is able to model new species, including the shape of an extinct animal, using only a few video frames. Additionally, the projected 3D shapes are accurate enough to facilitate the extraction of a realistic texture map from multiple frames.
The dataset about Shape Deformation of Animals consists of deformed meshes for horse, camel, lion,elephant, flamingo, face models.
Every mesh is triangulated and in .obj format. This is a line-based ASCII text format. Comment lines begin with #, vertices with v, vertex normals with vn, and triangles with f. The triangle lines contain indices into the vertex and vertex normal arrays. These indices are one-based (ie, starting with one -- not with zero). No texture coordinates are included in any of the meshes.
Each directory contains one mesh with -reference in the filename. This means that it was the reference mesh for that particular example, as indicated in the figures from the paper. The horse, cat, and face were used as source meshes. The camel, lion, head, flamingo, and elephant were target meshes. Thus, the poses in the target mesh directories were created by deforming the reference mesh according to the technique described in the paper.
References:
Robert W. Sumner, Jovan Popovic. Deformation Transfer for Triangle Meshes. ACM Transactions on Graphics. 23, 3. August 2004.
An unauthorized or an accidental change in the view of a surveillance camera is called a tampering. UHCTD is a large scale synthetic dataset for camera tampering detection. The dataset is created from two outdoor surveillance cameras, Camera A and Camera B. Camera A has a resolution of 2048X1536, and Camera B has a resolution of 1280X960. The videos are cropped to a 3 rd of their resolution during synthesis. Camera A has a framerate of 3 frames per second (fps), and Camera B has a framerate of 10 fps. The two cameras together capture a wide variety of regular scene changes that occur in surveillance cameras. We define four classes: normal, covered, defocussed, and moved. The tampers are synthesized into the videos using image processing techniques.
A large number of applications using unmanned aerial vehicle (UAV) sensors and platforms is being developed, for agriculture, logistics, recreational and military purposes. A branch of these applications uses the UAV exclusively for remote sensing purposes (RS), acquiring either top-view or oblique data that can be further processed at a centralized node.
Simultaneously, being at the core of video surveillance analysis, growing research efforts have been putted in the development of pedestrian re-identification and search methods able to work in real-world conditions, which is seen as a grand challenge. In particular, the problem of identifying pedestrians in crowded scenes based on very low resolution and partially occluded data becomes much harder in the multi-camera/multi-session mode, when matching data acquired in different places and with time lapses that deny the use of clothing information.
To date, the evaluation of pedestrian identification techniques has been conducted mostly on tracking databases (such as PETS, VIPeR, ETHZ and i-LIDS), with limited availability of soft biometric information, or even on gait recognition datasets (e.g., CASIA), which data acquisition conditions are highly dissimilar of the typical occurring in surveillance environments.
As a tool to support the research on pedestrian detection, tracking, re-identification and search methods, the P-DESTRE is a multi-session dataset of videos of pedestrians in outdoor public environments, fully annotated at the frame level for:
1) ID. Each pedestrian has a unique identifier that is kept among the data acquisition sessions, which enables to use the dataset for pedestrian re-identification;
2) Bounding box. The relative position of each pedestrian in the scene is provided as a bounding box, for every frame of the dataset, which also enables to use the data for object detection/semantic segmentation purposes;
3) Soft biometrics. Each subject of the dataset is fully characterised using 16 labels: gender, age, height, body volume, ethnicity, hair color, hairstyle, beard, mustache, glasses, head accessories, action, accessories and clothing information (x3), which enables to use the dataset also for evaluating soft biometrics inference and inference techniques.