---
language:
- en
pretty_name: NYUv2
tags:
- robotics
license: mit
task_categories:
- depth-estimation
- image-segmentation
- image-feature-extraction
dataset_info:
  features:
  - name: id
    dtype: string
  - name: rgb
    dtype: image
  - name: depth
    dtype: image
  - name: semantic
    dtype: image
  - name: instance
    dtype: image
  splits:
  - name: train
    num_bytes: 2237596040.926
    num_examples: 1014
  - name: val
    num_bytes: 639427936.0
    num_examples: 290
  - name: test
    num_bytes: 320361261.0
    num_examples: 145
  download_size: 1284403057
  dataset_size: 3197385237.926
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: test
    path: data/test-*
---

# NYUv2

NYUv2 is a benchmark RGB-D dataset widely used for scene understanding tasks such as:

- Indoor semantic segmentation
- Depth estimation
- Instance segmentation

- **Homepage:** [NYU Depth Dataset V2](https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html)
- **Paper:** [Indoor Segmentation and Support Inference from RGBD Images](https://cs.nyu.edu/~fergus/datasets/indoor_seg_support.pdf)

This version has been preprocessed to include aligned:
- RGB images (`.png`)
- Depth maps in millimeters (`.tiff`, `uint16`)
- Semantic masks (`.tiff`, scaled `uint16`)
- Instance masks (`.tiff`, scaled `uint16`)

Each sample is annotated with a consistent `id` and split across train/val/test.

## 🧾 Dataset Metadata

Additional files included:
- `camera_params.json` — camera intrinsics and distortion
- `class_names.json` — mapping from class IDs to human-readable names
- `scaling_factors.json` — used for label/mask de-scaling during training

## 📄 Citation

If you use this dataset, please cite the original authors:

```bibtex
@inproceedings{Silberman:ECCV12,
  author    = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus},
  title     = {Indoor Segmentation and Support Inference from RGBD Images},
  booktitle = {ECCV},
  year      = {2012}
}