---
dataset_info:
- config_name: default
  features:
  - name: image
    dtype: image
  - name: depth
    dtype: image
  - name: label
    dtype: image
  splits:
  - name: train
    num_bytes: 1829417279.14
    num_examples: 5285
  - name: test
    num_bytes: 1747976639.6
    num_examples: 5050
  download_size: 2452649738
  dataset_size: 3577393918.74
- config_name: uint8
  features:
  - name: image
    dtype:
      image:
        mode: RGB
  - name: depth
    dtype:
      image:
        mode: L
  - name: label
    dtype:
      image:
        mode: L
  splits:
  - name: train
    num_bytes: 673574397.52
    num_examples: 5285
  - name: test
    num_bytes: 598216510.95
    num_examples: 5050
  download_size: 916719066
  dataset_size: 1271790908.47
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
- config_name: uint8
  data_files:
  - split: train
    path: uint8/train-*
  - split: test
    path: uint8/test-*
---

# SUN RGB-D

Easier version for semantic segmentation. 

`default` config contains RGB and uint16 version of depth images. 

`uint8` config contains RGB and uint8 version of depth images, I convert uint16 by divide the pixel values by 255 and save it in new depth image. 

Comes from: 

SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite (CVPR 2015) ([PDF](https://rgbd.cs.princeton.edu/paper.pdf)) ([Website](https://rgbd.cs.princeton.edu/))