28 datasets found

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

The dataset used for depth estimation and object detection.

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...

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

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 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.

OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas.

Depth estimation is a crucial step for 3D reconstruction with panorama images in recent years. Panorama images maintain the complete spatial information but introduce distortion...

The dataset used in this paper is an ICL-NUIM dataset, which contains images and depth maps.

The NYU-Depth V2 dataset contains pairs of RGB and depth images collected from Microsoft Kinect in 464 indoor scenes.

KeystoneDepth is a collection of over 10,000 rectified antique stereographs of historical scenes captured between 1860 and 1963.

Monocular depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment.

KITTI 2012 is a real-world dataset in the outdoor scenario, and contains 194 training and 195 testing stereo image pairs with the size of 376 × 1240.

The ClearPose dataset consists of 63 objects arranged in numerous configurations.