Motion Estimation

DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface. Source: DDD17: End-To-End DAVIS Driving Dataset Image Source: DVS Driving Dataset 2017 (DDD17) README

PST900 is a dataset of 894 synchronized and calibrated RGB and Thermal image pairs with per pixel human annotations across four distinct classes from the DARPA Subterranean Challenge. Source: https://arxiv.org/abs/1909.10980 Image Source: https://github.com/ShreyasSkandanS/pst900_thermal_rgb

SegTHOR (Segmentation of THoracic Organs at Risk) is a dataset dedicated to the segmentation of organs at risk (OARs) in the thorax, i.e. the organs surrounding the tumour that must be preserved from irradiations during radiotherapy. In this dataset, the OARs are the heart, the trachea, the aorta and the esophagus, which have varying spatial and appearance characteristics. The dataset includes 60 3D CT scans, divided into a training set of 40 and a test set of 20 patients, where the OARs have been contoured manually by an experienced radiotherapist. Source: SegTHOR: Segmentation of Thoracic Organs at Risk in CT images Image Source: Lambert et al

Includes accurate pixel-wise motion masks, egomotion and ground truth depth. Source: EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras

Fisheye dataset comprises of synthetically generated fisheye sequences and fisheye video sequences captured with an actual fisheye camera designed for fisheye motion estimation. Source: A data set providing synthetic and real-world fisheye video sequences Image Source: https://arxiv.org/pdf/2211.17030v1.pdf

Provides a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track. Source: ADVIO: An authentic dataset for visual-inertial odometry

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 divided in training and testing are accessible through our website.

This dataset contains panoramic video captured from a helmet-mounted camera while riding a bike through suburban Northern Virginia. Source: Headcam

The Retinal Microsurgery dataset is a dataset for surgical instrument tracking. It consists of 18 in-vivo sequences, each with 200 frames of resolution 1920 × 1080 pixels. The dataset is further classified into four instrument-dependent subsets. The annotated tool joints are n=3 and semantic classes c=2 (tool and background). Source: Concurrent Segmentation and Localization for Tracking of Surgical Instruments Image Source: https://sites.google.com/site/sznitr/research/retinalmicrosurgery

Collections of images of the same rotating plastic object made in X-ray and visible spectra. Both parts of the dataset contain 400 images. The images are maid every 0.5 degrees of the object axial rotation. The collection of images is designed for evaluation of the performance of circular motion estimation algorithms as well as for the study of X-ray nature influence on the image analysis algorithms such as keypoints detection and description. Source: X-ray and Visible Spectra Circular Motion Images Dataset