A new dataset with significant occlusions related to object manipulation.
4 PAPERS • NO BENCHMARKS YET
Dataset consist of both real captures from Photoneo PhoXi structured light scanner devices annotated by hand and synthetic samples produced by custom generator. In comparison with existing datasets for 6D pose estimation, some notable differences include:
1 PAPER • 1 BENCHMARK
Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. To tackle this issue with a common benchmark, we introduce the Drunkard’s Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality.
This data set contains over 600GB of multimodal data from a Mars analog mission, including accurate 6DoF outdoor ground truth, indoor-outdoor transitions with continuous cross-domain ground truth, and indoor data with Optitrack measurements as ground truth. With 26 flights and a combined distance of 2.5km, this data set provides you with various distinct challenges for testing and proofing your algorithms. The UAV carries 18 sensors, including a high-resolution navigation camera and a stereo camera with an overlapping field of view, two RTK GNSS sensors with centimeter accuracy, as well as three IMUs, placed at strategic locations: Hardware dampened at the center, off-center with a lever arm, and a 1kHz IMU rigidly attached to the UAV (in case you want to work with unfiltered data). The sensors are fully pre-calibrated, and the data set is ready to use. However, if you want to use your own calibration algorithms, then the raw calibration data is also ready for download. The cross-domai
1 PAPER • NO BENCHMARKS YET
KITTI-6DoF is a dataset that contains annotations for the 6DoF estimation task for 5 object categories on 7,481 frames.