3D Object Detection From Monocular Images

The Waymo Open Dataset is comprised of high resolution sensor data collected by autonomous vehicles operated by the Waymo Driver in a wide variety of conditions. The Waymo Open Dataset currently contains 1,950 segments. The authors plan to grow this dataset in the future. Currently the datasets includes: * 1,950 segments of 20s each, collected at 10Hz (390,000 frames) in diverse geographies and conditions * Sensor data * 1 mid-range lidar * 4 short-range lidars * 5 cameras (front and sides) * Synchronized lidar and camera data * Lidar to camera projections * Sensor calibrations and vehicle poses * Labeled data * Labels for 4 object classes - Vehicles, Pedestrians, Cyclists, Signs * High-quality labels for lidar data in 1,200 segments * 12.6M 3D bounding box labels with tracking IDs on lidar data * High-quality labels for camera data in 1,000 segments * 11.8M 2D bounding box labels with tracking IDs on camera data

KITTI-360 is a large-scale dataset that contains rich sensory information and full annotations. It is the successor of the popular KITTI dataset, providing more comprehensive semantic/instance labels in 2D and 3D, richer 360 degree sensory information (fisheye images and pushbroom laser scans), very accurate and geo-localized vehicle and camera poses, and a series of new challenging benchmarks.

The dataset is designed specifically to solve a range of computer vision problems (2D-3D tracking, posture) faced by biologists while designing behavior studies with animals. Typically, datasets for animal-specific vision tasks are created using open-source video material. This might be effective for an initial start, but these methods are not deployment ready for the behavior community. Therefore, we designed a semi-automated method for biologists to create well-curated datasets at a large scale for the ML and Vision community. 3D-POP is the first dataset with 3D ground truth for multi-animal, multi-view tracking problems. Highlight: The dataset is captured with the intention of using it for various vision problems and with different levels of complexity (no of cameras, no of individuals) Video explanation: Link to YouTube video Video teaser: Link to YouTube video ## Dataset Features: ### Marker-based videos: - 6 hours+ of annotations of 18 individuals (groups of 1, 2, 5, 10). - Bounding box - Trajectories (2D and 3D) - Posture (2D and 3D) with 9 key points - Identities - Total of 57 sequences (4K) with 4 views. - Dataset customization* (Users can modify the dataset and add key points to the dataset) ### Markerless: - 1Hr+ videos of 18 individuals in groups of 1, 2, 5, 11. The birds have no markers. This data is provided as test cases and unsupervised approaches. ## Problems: ### 2D domain: - Position, Posture of birds (different group sizes n = 1, 2, 5, 10) with Single/Multiview. - Tracking with single - multiview ### 3D domain: - Position, Posture of birds (different group sizes n = 1, 2, 5, 10) with Single/Multiview. - Tracking with single - multiview ### Fine-grained recognition: - Identity tracking with ground truth. ### Unsupervised learning: - 2D or 3D posture problems ## Idea: The dataset is created with a motion capture system, using the 6-DOF tracking ability. Assumptions are that head and body act as rigid bodies when birds walk and forage (proved with experiment). Therefore, we get the 3D position of key points by tracking head/body orientation.