19 datasets found

The ETH3D dataset is a large-scale dataset for multi-view stereo reconstruction. It contains 120 scenes with complex geometric layouts.

The Middlebury and ETH3D datasets are used for stereo matching and 3D reconstruction tasks.

The KITTI 2015 and KITTI 2012 datasets are used for stereo matching and object detection tasks.

The dataset used for zoom and learn, a method for generalizing deep stereo matching.

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.

The dataset used for training and testing the proposed EDNet model for efficient disparity estimation.

The Middlebury 2014 dataset is a benchmark for stereo matching, consisting of 33 pairs of stereo images with sparse depth ground truth.

A benchmark for stereo matching, consisting of 50 stereo pairs with ground truth disparity maps.

A benchmark for stereo matching, consisting of 11 stereo pairs with ground truth disparity maps.

A benchmark for stereo matching, consisting of 75 stereo pairs with ground truth disparity maps.

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

The KITTI 2012 and 2015 datasets are used for stereo matching experiments.

KITTI 2015 dataset contains videos in 200 street scenes captured by RGB cameras, with sparse depth ground truths captured by Velodyne laser scanner.

Large dataset for stereo matching, optical flow, and scene flow estimation

The DTU dataset is a large-scale dataset for multi-view stereo depth inference. It contains over 100 scans taken under 7 different lighting conditions and fixed camera...

Object scene flow is a dataset for stereo matching and optical flow estimation.

Disparity prediction from stereo images is essential to computer vision applications including autonomous driving, 3D model reconstruction, and object detection.

The dataset used in the paper is the KITTI dataset, which is a benchmark for monocular depth estimation. The dataset consists of a large collection of images and corresponding...

Stereo matching aims to recover the dense reconstruction of unknown scenes by computing the disparity from rectified stereo images, helping robots intelligently interact with...