40 datasets found

Depth completion dataset for industrial objects with RGBD images and ground truth depth.

A robust framework for vision-based roadside 3D object detection

The dataset used for depth estimation and object detection.

ETH3D is a small real-world grayscale dataset with both indoor and outdoor scenes. It contains 27 labeled stereo image pairs for training and 20 stereo pairs for testing.

SCARED is one of the MICCAI 2019 challenges, and provided a training set acquired from 7 pigs. Each pig corresponds to 5 videos recorded by a binocular camera from 5 different...

A dataset for visual odometry, containing 11 sequences with provided ground-truth poses.

The FlyingThings3D dataset is a benchmark for stereo matching, consisting of a large collection of images and corresponding disparity maps.

A dataset for pupil diameter measurement in VR, generated using the Dynamic Periocular Data Generation (DPDG) environment.

A dataset for periocular depth estimation in VR imagery, generated using the Dynamic Periocular Data Generation (DPDG) environment.

The NYUD-v2 dataset is a benchmark for indoor scene segmentation and depth estimation. It contains 1449 images with 4 tasks: semantic segmentation, depth estimation, surface...

Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging vision problem that aims to predict panoptic segmentation and depth in a video simultaneously.

The KITTI odometry dataset is a collection of 22 sequences, containing point clouds, images, and GPS recordings of inner-city traffic, residential areas, highway scenes, and...

The dataset used for training the D2-Net model, consisting of 327,036 image pairs with at least 50% overlap in the sparse SfM point cloud.

The Virtual KITTI dataset is a synthetic dataset, where the virtual scenes are cloned from the real world KITTI video sequences. Besides the 5 virtual image sequences cloned...

The dataset for Symmetry-awareDepthEstimationusingDeepNeuralNetworks, containing product images with reflection symmetry.

The dataset is used for depth estimation from monocular images and sparse radar data.

NYUD2 dataset is a dataset containing RGB-D images for indoor scene understanding. It is used for depth estimation and semantic segmentation tasks.

The NYUv2 dataset is a large-scale dataset for 3D object recognition and semantic segmentation. It contains 206 test set video sequences with 135 classes.

End-to-end Davis driving dataset.

Depth estimation, visual odometry, and bird’s-eye-view scene layout estimation present three critical tasks for driving scene perception, which is fundamental for motion...