17 datasets found

The dataset used for monocular depth estimation task

The dataset used for monocular depth estimation task

The KITTI-C dataset is a robustness benchmark for outdoor monocular depth estimation under data corruptions.

Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?

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.

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

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.

Monocular depth estimation dataset

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 proposed TransDepth framework for pixel-wise prediction problems involving continuous labels.

DepthP+P: A method for self-supervised monocular depth estimation using planar and parallax.

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

Self-supervised monocular depth estimation has gathered no-table interest since it can liberate training from dependency on depth annotations. In monocular video training case,...

Dense pixel matching is required for many computer vision algorithms such as disparity, optical flow or scene flow estimation. Feature Pyramid Networks (FPN) have proven to be a...

The dataset used in the paper is the KITTI Vision Benchmark and Cityscapes dataset for monocular depth estimation and structure prediction.

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...