15 datasets found

The KITTI Visual Odometry benchmark and the Malaga 2013 dataset are used to test the proposed LS-VO architecture.

The iBIMS-1 dataset used for testing the CodedVO method, containing five zero-shot scenes.

The ICL-NUIM dataset used for testing the CodedVO method, containing two scenes: (of-krt2) and (lr-krt2).

The dataset used for training and testing the CodedVO method, containing three indoor sequences: LivingRoom, DiningRoom, and Corridor.

This paper presents a benchmark for visual odometry and SLAM.

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

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...

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.

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 TUM RGB-D dataset is a large-scale benchmark for RGB-D SLAM.

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...