README.md

metadata

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
• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: NYU Depth Dataset V2 homepage
• Repository: Fast Depth repository 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.
• Paper: Indoor Segmentation and Support Inference from RGBD Images and FastDepth: Fast Monocular Depth Estimation on Embedded Systems
• Point of Contact: Nathan Silberman and Diana Wofk

Dataset Summary

As per the dataset homepage:

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 (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.