Consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations
13 PAPERS • NO BENCHMARKS YET
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 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
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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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…In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects.
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…The datasets contains more than 100 scenes, each of which is 8 seconds long, and provides 28 types of labels for object classification and 37 types of annotations for semantic segmentation.
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…For each image, the dataset contains the 3D poses, per-pixel class segmentation, and 2D/3D bounding box coordinates for all objects.
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…Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities.
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…Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment.
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…In order to ease multitask learning, we provide a pairing of 2D instance segments with 3D bounding boxes.
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