Datasets:
Tasks:
Depth Estimation
Modalities:
Image
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
depth-estimation
License:
| license: apache-2.0 | |
| language: | |
| - en | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - depth-estimation | |
| task_ids: | |
| - [] | |
| pretty_name: NYU Depth V2 | |
| tags: | |
| - depth-estimation | |
| paperswithcode_id: nyuv2 | |
| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: depth_map | |
| dtype: image | |
| splits: | |
| - name: train | |
| num_bytes: 20212097551 | |
| num_examples: 47584 | |
| - name: validation | |
| num_bytes: 240785762 | |
| num_examples: 654 | |
| download_size: 35151124480 | |
| dataset_size: 20452883313 | |
| # Dataset Card for MIT Scene Parsing Benchmark | |
| ## Table of Contents | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks](#supported-tasks) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** [NYU Depth Dataset V2 homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html) | |
| - **Repository:** Fast Depth [repository](https://github.com/dwofk/fast-depth) which was used to source the dataset in this repository. It is a preprocessed version of the original NYU Depth V2 dataset linked above. It is also used in [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/nyu_depth_v2). | |
| - **Paper:** [Indoor Segmentation and Support Inference from RGBD Images](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) and [FastDepth: Fast Monocular Depth Estimation on Embedded Systems](https://arxiv.org/abs/1903.03273) | |
| - **Point of Contact:** [Nathan Silberman](mailto:silberman@@cs.nyu.edu) and [Diana Wofk](mailto:[email protected]) | |
| ### Dataset Summary | |
| As per the [dataset homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html): | |
| 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](http://www.xbox.com/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 (cup1, cup2, cup3, etc) | |
| 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. | |
| ### Supported Tasks | |
| - `depth-estimation`: Depth estimation is the task of approximating the perceived depth of a given image. In other words, it's about measuring the distance of each image pixel from the camera. |