14 datasets found

The dataset used for training and evaluation of the proposed framework for unsupervised metric relocalization.

Robust edge-based visual odometry using machine-learned edges.

Visual odometry algorithm that efficiently utilizes edge pixels to find the relative pose that minimizes the photometric error between images.

The KITTI Visual Odometry benchmark dataset consists of 22 stereo sequences with ground truth trajectories.

A dataset for visual odometry, containing 11 sequences with provided ground-truth poses.

The KITTI odometry dataset is a collection of 22 sequences, containing point clouds, images, and GPS recordings of inner-city traffic, residential areas, highway scenes, and...

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.

The KITTI odometry dataset is a benchmark for evaluating visual odometry and other computer vision tasks. It contains a large collection of images and corresponding ground-truth...

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

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 KITTI Vision Benchmark Suite is a dataset used for object detection and tracking in autonomous vehicles.

The dataset used in the paper is a Sintel dataset, which consists of low-resolution optical flow maps and their corresponding high-resolution RGB images.

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...