This dataset inclue multi-spectral acquisition of vegetation for the conception of new DeepIndices. The images were acquired with the Airphen (Hyphen, Avignon, France) six-band multi-spectral camera configured using the 450/570/675/710/730/850 nm bands with a 10 nm FWHM. The dataset were acquired on the site of INRAe in Montoldre (Allier, France, at 46°20'30.3"N 3°26'03.6"E) within the framework of the “RoSE challenge” founded by the French National Research Agency (ANR) and in Dijon (Burgundy, France, at 47°18'32.5"N 5°04'01.8"E) within the site of AgroSup Dijon. Images of bean and corn, containing various natural weeds (yarrows, amaranth, geranium, plantago, etc) and sowed ones (mustards, goosefoots, mayweed and ryegrass) with very distinct characteristics in terms of illumination (shadow, morning, evening, full sun, cloudy, rain, ...) were acquired in top-down view at 1.8 meter from the ground. (2020-05-01)
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…Also, it provides sky segmentation masks, instance segmentation masks as well as invalid pixel masks.
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The ScanNet200 benchmark studies 200-class 3D semantic segmentation - an order of magnitude more class categories than previous 3D scene understanding benchmarks. The source of scene data is identical to ScanNet, but parses a larger vocabulary for semantic and instance segmentation
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…The dataset contains annotations for left and right views that include: camera intrinsics and extrinsics, image depth, instance segmentation masks, binary foreground / background segmentation masks, optical
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One-Shot Affordance Part Segmentation variant of the UMD dataset. Each object instance in the dataset contains a single image.
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…for each object: 600 12 megapixel images, sampling the viewing hemisphere 600 registered RGB-D point clouds from a Carmine 1.09 sensor Pose information for each of the above images and point clouds Segmentation masks for each of the above images (and segmented point clouds) Merged point clouds consisting of data from all 600 viewpoints Reconstructed meshes from the merged point clouds Paper: ICRA 2014 "A Large-Scale
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The RMRC 2014 indoor dataset is a dataset for indoor semantic segmentation. It employs the NYU Depth V2 and Sun3D datasets to define the training set. The test data consists of newly acquired images.
…The dataset may be used for evaluation of different perception algorithms for segmentation, detection, classification, etc. The dataset creators provide manually annotated pixel-wise ground truth segmentation masks for 6 classes: Obstacle, Trail, Sky, Grass, Vegetation, and Void.
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…Each RGB image has a corresponding depth and segmentation map. As many as 700 object categories are labeled. The training and testing sets contain 5285 and 5050 images, respectively.
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…Robotic grasping requires a variety of computer vision tasks such as object detection, segmentation, grasp prediction, pick planning, etc. The proposed dataset contains 100,000 images and 25 different object types, and is split into 5 difficulties to evaluate object detection and segmentation model performance in different grasping scenarios We also propose a new layout-weighted performance metric alongside the dataset for evaluating object detection and segmentation performance in a manner that is more appropriate for robotic grasp applications This repository contains the first phase of MetaGraspNet benchmark dataset which includes detailed object detection, segmentation, layout annotations, and a script for layout-weighted performance metric
…Each image is annotated with various low/high-level vision modalities, including semantic segmentation, depth, surface normals, intrinsic colors, and optical flow.
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…High quality sparese instance segmentation labels.
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…Each frame has a semantic segmentation of the objects in the scene and information about the camera pose. It is composed by 415 sequences captured in 254 different spaces, in 41 different buildings.
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…Each scene is a residential building consisting of multiple rooms and floor levels, and is annotated with surface construction, camera poses, and semantic segmentation.
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…Object segmentation masks, object poses and object attributes are provided. In addition, synthetic images generated using 330 3D object models are used to augment the dataset. FewSOL dataset can be used to study a set of few-shot object recognition problems such as classification, detection and segmentation, shape reconstruction, pose estimation, keypoint correspondences and
…By collecting data in simulations, multi-modal sensor data and precise ground truth labels are obtainable such as the RGB image, depth image, semantic segmentation, change segmentation, camera poses, and
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…In total, 103 scenes of 10 common off-the-shelf objects with rich textures are recorded, with each frame annotated with a per-pixel semantic segmentation and ground-truth object poses provided by a commercial
…Uncertainties for Autonomous Driving), consisting of 10,413 realistic synthetic images with diverse adverse weather conditions (night, fog, rain, snow), out-of-distribution objects, and annotations for semantic segmentation
…It comprises a vast collection of 333,819 images and 4,000,056 annotations, providing instance-level segmentation masks, ground-truth poses, and completed depth information.
…In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects.
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…Each sample provides: RGB image (320x320 pixels) Depth map (320x320 pixels) Segmentation masks (320x320 pixels) for the classes: background, person, three classes for each finger and one for each palm
…We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation.
…To gather the simulated dataset, we captured before and after flood pairs from 2000 viewpoints with the following modalities: non-flooded RGB image, depth map, segmentation map flooded RGB image, binary mask of the flooded area, segmentation map The camera was placed about 1.5 above ground, and has a field of view of 120 degree, and the resolution of the images is 1200 x 900.
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…Furthermore, for each image the dataset creators provide pixel-wise semantic segmentation annotations for ten categories: Background, Sky, Road, Sidewalk, Grass, Vegetation, Building, Poles & Fences, Dynamic This dataset can be very challenging for vision based applications such as global localization, camera relocalization, semantic segmentation, visual odometry and loop closure detection, as it contains
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…This dataset can also be used for other learning use-cases, like instance segmentation or depth estimation. Or where household objects or continual learning are of interest. In addition, we also provide a corresponding depth, segmentation, and normal image. In addition to the RGB, depth, segmentation, and normal images, we also provide the calculated features of the RGB images (by ResNet50) as used in our RECALL paper.
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…The main purpose of OCID is to allow systematic comparison of existing object segmentation methods in scenes with increasing amount of clutter.
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…It consists of an exhaustive list of actions performed while driving and multi-modal labeled images (depth, RGB and IR), with complete annotations for 2D and 3D object detection, instance and semantic segmentation
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…We benchmark four foundational video understanding tasks: action recognition, action segmentation, object detection and multi-object tracking.
…Tasks our dataset support: Generaliazable Novel view synthesis (Few shot evaluation) Novel view synthesis (Overfitting evaluation) 6D pose estimation Object editing Depth estimation Semantic Segmentation Instance Segmentation
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…Overall this dataset covers a wide range of perception related tasks such as loop closure detection, semantic segmentation, visual odometry estimation, global localization, scene flow estimation and behavior
…Squats Bird Dogs Supermans Bicycle Crunches Leg Raises Front Raises (with dumbbells) Overhead Press (with dumbbells) Annotations The dataset includes the following annotations: Bounding boxes Segmentation
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…They include body height, weight, and segment lengths measured before the beginning of a recording session.
…The aim of the present study is to provide insights on sign language recognition, focusing on mapping non-segmented video streams to glosses.