# Depth Estimation

Depth estimation is the task of predicting depth of the objects present in an image.

Inputs
Depth Estimation Model
Output

## Use Cases

Depth estimation models can be used to estimate the depth of different objects present in an image.

### Estimation of Volumetric Information

Depth estimation models are widely used to study volumetric formation of objects present inside an image. This is an important use case in the domain of computer graphics.

### 3D Representation

Depth estimation models can also be used to develop a 3D representation from a 2D image.

There are two depth estimation subtasks.

• Absolute depth estimation: Absolute (or metric) depth estimation aims to provide exact depth measurements from the camera. Absolute depth estimation models output depth maps with real-world distances in meter or feet.

• Relative depth estimation: Relative depth estimation aims to predict the depth order of objects or points in a scene without providing the precise measurements.

## Inference

With the transformers library, you can use the depth-estimation pipeline to infer with image classification models. You can initialize the pipeline with a model id from the Hub. If you do not provide a model id it will initialize with Intel/dpt-large by default. When calling the pipeline you just need to specify a path, http link or an image loaded in PIL. Additionally, you can find a comprehensive list of various depth estimation models at this link.

from transformers import pipeline

result = estimator(images="http://images.cocodataset.org/val2017/000000039769.jpg")
result

# {'predicted_depth': tensor([[[ 6.3199,  6.3629,  6.4148,  ..., 10.4104, 10.5109, 10.3847],
#           [ 6.3850,  6.3615,  6.4166,  ..., 10.4540, 10.4384, 10.4554],
#           [ 6.3519,  6.3176,  6.3575,  ..., 10.4247, 10.4618, 10.4257],
#           ...,
#           [22.3772, 22.4624, 22.4227,  ..., 22.5207, 22.5593, 22.5293],
#           [22.5073, 22.5148, 22.5114,  ..., 22.6604, 22.6344, 22.5871],
#           [22.5176, 22.5275, 22.5218,  ..., 22.6282, 22.6216, 22.6108]]]),
#  'depth': <PIL.Image.Image image mode=L size=640x480 at 0x7F1A8BFE5D90>}

# You can visualize the result just by calling result["depth"].


## Compatible libraries

Depth Estimation demo

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Models for Depth Estimation
Datasets for Depth Estimation

Note NYU Depth V2 Dataset: Video dataset containing both RGB and depth sensor data.

Note Monocular depth estimation benchmark based without noise and errors.

Spaces using Depth Estimation

Note An application that predicts the depth of an image and then reconstruct the 3D model as voxels.

Note An application on cutting-edge depth estimation.

Note An application to try state-of-the-art depth estimation.

Metrics for Depth Estimation

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