25 datasets found

A dataset for stereo saliency detection, containing 400 stereo images and estimated depth maps.

A dataset for stereo saliency detection, containing 1985 stereo images and estimated depth maps.

The AirSim-MAP dataset is a multi-agent perception dataset, where each agent has its own depth, pose, RGB images, and semantic segmentation masks.

Image segmentation and depth estimation are crucial tasks in computer vision, especially in autonomous driving scenarios.

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

A method for depth estimation from light field data, based on a fully convolutional neural network architecture.

Depth estimation in robotic surgery is vital in 3D recon-struction, surgical navigation and augmented reality visualization.

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 dataset used in the paper for light field depth estimation.

The dataset used in the paper for light field depth estimation.

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...

A weakly-supervised depth estimation network using attention mechanism for cases with wrong labels.

Large-scale data for multiple-view stereopsis.

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

Relative Depth in Stereo (RDIS) dataset is a dataset containing images labelled with dense relative depths. The ground-truth relative depths are obtained through existing stereo...

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.

A medium-scale synthetic 4D LFV dataset that can be used for training deep learning-based methods.

Depth-aware video frame interpolation.

Multi-task learning (MTL) research is broadly divided into two categories: one is to learn the correlation between tasks through model structures, and the other is to balance...

The dataset used in the paper is a spike dataset generated from RGB frames of four open access outdoor datasets, including Kitti, Driving Stereo, Driving Stereo Weather, and...