3D Character Animation From A Single Photo

ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects. Up to now, ScanNet v2, the newest version of ScanNet, has collected 1513 annotated scans with an approximate 90% surface coverage. In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects. Source: A Review of Point Cloud Semantic Segmentation Image Source: http://www.scan-net.org/

The ADE20K semantic segmentation dataset contains more than 20K scene-centric images exhaustively annotated with pixel-level objects and object parts labels. There are totally 150 semantic categories, which include stuffs like sky, road, grass, and discrete objects like person, car, bed. Source: Cooperative Image Segmentation and Restoration in Adverse Environmental Conditions Image Source: https://groups.csail.mit.edu/vision/datasets/ADE20K/

The Places205 dataset is a large-scale scene-centric dataset with 205 common scene categories. The training dataset contains around 2,500,000 images from these categories. In the training set, each scene category has the minimum 5,000 and maximum 15,000 images. The validation set contains 100 images per category (a total of 20,500 images), and the testing set includes 200 images per category (a total of 41,000 images). Source: Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs Image Source: http://places.csail.mit.edu/browser.html

The SUN RGBD dataset contains 10335 real RGB-D images of room scenes. 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. Source: Mix and match networks: multi-domain alignment for unpaired image-to-image translation Image Source: https://rgbd.cs.princeton.edu/

The Places365 dataset is a scene recognition dataset. It is composed of 10 million images comprising 434 scene classes. There are two versions of the dataset: Places365-Standard with 1.8 million train and 36000 validation images from K=365 scene classes, and Places365-Challenge-2016, in which the size of the training set is increased up to 6.2 million extra images, including 69 new scene classes (leading to a total of 8 million train images from 434 scene classes). Source: Semantic-Aware Scene Recognition Image Source: Places

The Scene UNderstanding (SUN) database contains 899 categories and 130,519 images. There are 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition. Image Source: The Selection of Useful Visual Words in Class-Imbalanced Image Classification

AID is a new large-scale aerial image dataset, by collecting sample images from Google Earth imagery. Note that although the Google Earth images are post-processed using RGB renderings from the original optical aerial images, it has proven that there is no significant difference between the Google Earth images with the real optical aerial images even in the pixel-level land use/cover mapping. Thus, the Google Earth images can also be used as aerial images for evaluating scene classification algorithms. The new dataset is made up of the following 30 aerial scene types: airport, bare land, baseball field, beach, bridge, center, church, commercial, dense residential, desert, farmland, forest, industrial, meadow, medium residential, mountain, park, parking, playground, pond, port, railway station, resort, river, school, sparse residential, square, stadium, storage tanks and viaduct. All the images are labelled by the specialists in the field of remote sensing image interpretation, and some samples of each class are shown in Fig.1. In all, the AID dataset has a number of 10000 images within 30 classes. The images in AID are actually multi-source, as Google Earth images are from different remote imaging sensors. This brings more challenges for scene classification than the single source images like UC-Merced dataset. Moreover, all the sample images per each class in AID are carefully chosen from different countries and regions around the world, mainly in China, the United States, England, France, Italy, Japan, Germany, etc., and they are extracted at different time and seasons under different imaging conditions, which increases the intra-class diversities of the data.

A new and challenging video database of dynamic scenes that more than doubles the size of those previously available. This dataset is explicitly split into two subsets of equal size that contain videos with and without camera motion to allow for systematic study of how this variable interacts with the defining dynamics of the scene per se. Source: Temporal Residual Networks for Dynamic Scene Recognition

Context This is the Original data provided by MIT . Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g., bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information. Content The database contains 67 Indoor categories, and a total of 15620 images. The number of images varies across categories, but there are at least 100 images per category. All images are in jpg format. The images provided here are for research purposes only. Acknowledgements Thanks to MIT Thanks to Aude Oliva for helping to create the database of indoor scenes. Funding for this research was provided by NSF Career award (IIS 0747120)

The AuDio Visual Aerial sceNe reCognition datasEt (ADVANCE) is a brand-new multimodal learning dataset, which aims to explore the contribution of both audio and conventional visual messages to scene recognition. This dataset in summary contains 5075 pairs of geotagged aerial images and sounds, classified into 13 scene classes, i.e., airport, sports land, beach, bridge, farmland, forest, grassland, harbor, lake, orchard, residential area, shrub land, and train station. Source:

An annotated dataset is released to enable dynamic scene classification that includes 80 hours of diverse high quality driving video data clips collected in the San Francisco Bay area. The dataset includes temporal annotations for road places, road types, weather, and road surface conditions. Source: Dynamic Traffic Scene Classification with Space-Time Coherence

HOWS-CL-25 (Household Objects Within Simulation dataset for Continual Learning) is a synthetic dataset especially designed for object classification on mobile robots operating in a changing environment (like a household), where it is important to learn new, never seen objects on the fly. 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. Our dataset contains 150,795 unique synthetic images using 25 different household categories with 925 3D models in total. For each of those categories, we generated about 6000 RGB images. In addition, we also provide a corresponding depth, segmentation, and normal image. The dataset was created with BlenderProc [Denninger et al. (2019)], a procedural pipeline to generate images for deep learning. This tool created a virtual room with randomly textured floors, walls, and a light source with randomly chosen light intensity and color. After that, a 3D model is placed in the resulting room. This object gets customized by randomly assigning materials, including different textures, to achieve a diverse dataset. Moreover, each object might be deformed with a random displacement texture. We use 774 3D models from the ShapeNet dataset [A. X. Chang et al. (2015)] and the other models from various internet sites. Please note that we had to manually fix and filter most of the models with Blender before using them in the pipeline! For continual learning (CL), we provide two different loading schemes: - Five sequences with five categories each - Twelve sequences with three categories in the first and two in the other sequences. 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. In those two loading schemes, ten percent of the images are used for validation, where we ensure that an object instance is either in the training or the validation set, not in both. This avoids learning to recognize certain instances by heart. We recommend using those loading schemes to compare your approach with others. For further information and code examples, please have a look at our website: https://github.com/DLR-RM/RECALL.

MAI is a dataset for multi-scene recognition in single aerial images. It consists of 3,923 labelled large-scale images from Google Earth imagery that covers the United States, Germany, and France. The size of each image is 512 ×512, and spatial resolutions vary from 0.3 m/pixel to 0.6 m/pixel. After capturing aerial images, multiple scene-level labels were manually assigned to each image from in total 24 scene categories, including apron, baseball, beach, commercial, farmland, woodland, parking lot, port, residential, river, storage tanks, sea, bridge, lake, park, roundabout, soccer field, stadium, train station, works, golf course, runway, sparse shrub, and tennis court

This dataset is an extremely challenging set of over 5000+ original India food images captured and crowdsourced from over 800+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs. ### Dataset Features - Dataset size : 5000+ - Captured by : Over 800+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 800+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points, dimlight etc. - Device used : Captured using mobile phones in 2020-2021 - Usage : Indian food classification, Dish classification, Food plate detection, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

Scene Recognition is a problem, where a set of visible objects must be correctly associated with objects marked on a semantic map - this problem is also sometimes called a Data Association. Please note, that Scene Recognition in terms where the observed scene must be labeled in terms such as 'kitchen', 'bedroom' and so on is a different problem. Scene Recognition is used in different mobile robot localization problems, such as semantic mapping, global localization, kidnapping, loop-closing and so on. But problem is that there is no special dataset for that task (at least I found none). Such dataset should have the following features: - a Semantic map with globally marked objects; - scenes data (views from robot camera), where objects are also labeled and their IDs are correctly matched with IDs on the Semantic map. For full info please follow https://github.com/MoscowskyAnton/scene_recognition_kitti_360

This dataset is an extremely challenging set of over 3000+ originally Stair images captured and crowdsourced from over 500+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 3000+ - Captured by : Over 500+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 500+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2022 - Usage : Stair detection , Stair Edge detection , Computer Vision , etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform