- 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.BuilderVersions:
0.0.1(default): No release notes.
Download size:
31.92 GiBDataset size:
74.03 GiBAuto-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_superviseddoc):('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}
}