KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. 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. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odome
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The 3DMATCH benchmark evaluates how well descriptors (both 2D and 3D) can establish correspondences between RGB-D frames of different views. The dataset contains 2D RGB-D patches and 3D patches (local TDF voxel grid volumes) of wide-baselined correspondences.
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DeformingThings4D is a synthetic dataset containing 1,972 animation sequences spanning 31 categories of humanoids and animals. It provides 200 animations for humanoids and 1772 animations for animals.
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UrbanLoco is a mapping/localization dataset collected in highly-urbanized environments with a full sensor-suite. The dataset includes 13 trajectories collected in San Francisco and Hong Kong, covering a total length of over 40 kilometers.
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Cross-source point cloud dataset for registration task. It includes point clouds from structure from motion (SFM), Kinect, Lidar.
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A benchmark for matching and registration of partial point clouds with time-varying geometry. It is constructed using randomly selected 1761 sequences from DeformingThings4D.
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The Cooperative Driving dataset is a synthetic dataset generated using CARLA that contains lidar data from multiple vehicles navigating simultaneously through a diverse set of driving scenarios. This dataset was created to enable further research in multi-agent perception (cooperative perception) including cooperative 3D object detection, cooperative object tracking, multi-agent SLAM and point cloud registration. Towards that goal, all the frames have been labelled with ground-truth sensor pose and 3D object bounding boxes.
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