The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. It features:

1449 densely labeled pairs of aligned RGB and depth images 464 new scenes taken from 3 cities 407,024 new unlabeled frames Each object is labeled with a class and an instance number. The dataset has several components: Labeled: A subset of the video data accompanied by dense multi-class labels. This data has also been preprocessed to fill in missing depth labels. Raw: The raw RGB, depth and accelerometer data as provided by the Kinect. Toolbox: Useful functions for manipulating the data and labels.

Processed versions of some open-source datasets for evaluation of monocular geometry estimation.

Dataset Source Publication Num images Storage Size Note

NYUv2 NYU Depth Dataset V2 [1] 654 243 MB Offical test split. Mirror, glass and window manually removed. Depth beyound 5 m truncated.

KITTI KITTI Vision Benchmark Suite [2, 3] 652 246 MB Eigen's test split.

ETH3D ETH3D SLAM & Stereo Benchmarks [4] 454 1.3 GB Downsized from 6202×4135 to 2048×1365

iBims-1 iBims-1 (independent… See the full description on the dataset page: https://huggingface.co/datasets/Ruicheng/monocular-geometry-evaluation.