{"repo":"google-research-datasets/Objectron","free":true,"listed":false,"github":"https://github.com/google-research-datasets/Objectron","clone":"git clone https://github.com/google-research-datasets/Objectron.git","description":"Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes","language":"Jupyter Notebook","stars":2348,"topics":["deep-learning","computer-vision","machine-learning","python","tensorflow","pytorch","3d-vision","3d-reconstruction","ai","3d"],"license":null,"category":"machine-learning","readme_excerpt":"Objectron Dataset Objectron is a dataset of short object centric video clips with pose annotations. --- Website • Dataset Format • Tutorials • License The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. The dataset consists of 15K annotated video clips supplemented with over 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops , and shoes . In addition, to ensure geo-diversity, our dataset is collected from 10 countries across five continents. Along with the dataset, we are also sharing a 3D object detection solution for four categories of objects — shoes, chairs, mugs, and cameras. These models are trained using this dataset, and are released in MediaPipe, Google's open source framework for cross-platform customizable ML solutions for live and streaming media. Key Features - 15000 annotated videos and 4M annotated images - All samples include high-res images, object pose, camera pose, point-cloud, and surface planes. - Ready to use examples in various tf.record formats, which can be used in Tensor","default_branch":null,"files":null,"tree":[],"storefront":"/r/google-research-datasets","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/google-research-datasets/Objectron/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}