nyu_depth_v2

• Description:

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.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html

• Source code: tfds.datasets.nyu_depth_v2.Builder

• Versions:

  • 0.0.1 (default): No release notes.

• Download size: 31.92 GiB

• Dataset size: 74.03 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'train'	47,584
'validation'	654

• Feature structure:

FeaturesDict({
    'depth': Tensor(shape=(480, 640), dtype=float16),
    'image': Image(shape=(480, 640, 3), dtype=uint8),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
depth	Tensor	(480, 640)	float16
image	Image	(480, 640, 3)	uint8

• Supervised keys (See as_supervised doc): ('image', 'depth')

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@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}
}
@inproceedings{icra_2019_fastdepth,
  author    = {Wofk, Diana and Ma, Fangchang and Yang, Tien-Ju and Karaman, Sertac and Sze, Vivienne},
  title     = {FastDepth: Fast Monocular Depth Estimation on Embedded Systems},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year      = {2019}
}