12 datasets found

A dataset and evaluation methodology for depth estimation on 4D light fields.

MUAD: A synthetic dataset for autonomous driving with multiple uncertainties and annotations for semantic segmentation, depth estimation, object detection, and instance...

The dataset used in the paper is a synthetic dataset for unsupervised depth estimation, which includes vKitti2, SHIFT, and UrbanSyn datasets.

The dataset used in the paper is a synthetic dataset for unsupervised depth estimation, which includes vKitti2, SHIFT, and UrbanSyn datasets.

The dataset used in the paper is a synthetic dataset for unsupervised depth estimation, which includes vKitti2, SHIFT, and UrbanSyn datasets.

The dataset used in the paper is a synthetic dataset for unsupervised depth estimation, which includes vKitti2, SHIFT, and UrbanSyn datasets.

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 NYUDv2 dataset contains 1,449 labeled indoor-scene RGB images with both parsing annotations and Kinect depths.

The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and...

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...

The KITTI 2015 dataset is a real-world dataset of street views, containing 200 training stereo image pairs with sparsely labeled disparity from LiDAR data.

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