midasnet

MidasNet is a model for monocular depth estimation trained by mixing several datasets; as described in the following paper: "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer" https://arxiv.org/abs/1907.01341

The model input is a blob that consists of a single image of "1x3x384x384" in `RGB`

order.

The model output is an inverse depth map that is defined up to an unknown scale factor.

NOTE: Originally the model weights are stored at Google Drive,

which is unstable to download from due to weights size. Weights were additionally uploaded to https://download.01.org/opencv/public_models, OpenVINO Model Downloader uses this location for downloading.

See here

Metric | Value |
---|---|

Type | Monodepth |

GFLOPs | 207.4915 |

MParams | 104.0814 |

Source framework | PyTorch* |

Metric | Value |
---|---|

rmse | 7.5878 |

Image, name - `image`

, shape - `1,3,384,384`

, format is `B,C,H,W`

where:

`B`

- batch size`C`

- channel`H`

- height`W`

- width

Channel order is `RGB`

.

Mean values - [123.675, 116.28, 103.53]. Scale values - [51.525, 50.4, 50.625].

Image, name - `image`

, shape - `1,3,384,384`

, format is `B,C,H,W`

where:

`B`

- batch size`C`

- channel`H`

- height`W`

- width

Channel order is `BGR`

.

Inverse depth map, name - `inverse_depth`

, shape - `1,384,384`

, format is `B,H,W`

where:

`B`

- batch size`H`

- height`W`

- width

Inverse depth map is defined up to an unknown scale factor.

Inverse depth map, name - `inverse_depth`

, shape - `1,384,384`

, format is `B,H,W`

where:

`B`

- batch size`H`

- height`W`

- width

Inverse depth map is defined up to an unknown scale factor.

The original model is released under the following license:

MIT License

Copyright (c) 2019 Intel ISL (Intel Intelligent Systems Lab)

Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

SOFTWARE.

[*] Other names and brands may be claimed as the property of others.