FPv1 (prior name FAUST-partial) is a 3D registration benchmark dataset created to address the lack of data variability in the existing 3D registration benchmarks such as: 3DMatch, ETH, KITTI.
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FAUST-partial is a 3D registration benchmark dataset created to provide a more informative evaluation of 3D registration methods. The dataset addresses two main limitations of current 3D registration benchmarks:
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This dataset presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing datasets by simultaneously addressing several issues, such as environment diversity, motion patterns, and sensor frequency. It uses up-to-date devices and presents effective procedures to accurately calibrate the intrinsic and extrinsic of the sensors while addressing temporal synchronization. During recording, we cover multi-floor buildings, gardens, urban and highway scenarios. Combining handheld and car-based data collections, our setup can simulate any robot (quadrupeds, quadrotors, autonomous vehicles). The dataset includes an accurate 6-dof ground truth based on a novel methodology that refines the RTK-GPS estimate with LiDAR point clouds through Bundle Adjustment. All sequences divi
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