Method for 3D reconstruction from satellite imagery
AssigneeMaxar International Sweden AB
InventorsTim Yngesjö, Carl Sundelius, Anton Nordmark
Life of this patent
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Abstract
The present disclosure relates to a method for 3D reconstruction from satellite imagery using deep learning, said method comprising providing (101) at least two overlapping 2D satellite images, providing (102) imaging device parameters for the at least two overlapping 2D satellite images, providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view geocoded 3D ground truth data and predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters.
Claims21
A method for 3D reconstruction from satellite imagery using deep learning, said method comprising the steps of:
- providing (101) at least two overlapping 2D satellite images,
- providing (102) imaging device parameters for the at least two overlapping 2D satellite images,
- providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view 3D geocoded ground truth data, and
- predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters,
- wherein the prediction of the depth map comprises predicting also an uncertainty map relating to the depth map.
A method for 3D reconstruction from satellite imagery using deep learning, said method comprising the steps of:
- providing (101) at least two overlapping 2D satellite images,
- providing (102) imaging device parameters for the at least two overlapping 2D satellite images,
- providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view 3D geocoded ground truth data, and
- predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters,
- wherein the MLN comprises a plurality of MLNs each predicting a depth map and wherein an updated depth map is formed based on the predicted depth maps and when available based on the uncertainty map relating to the respective depth map.
A method for 3D reconstruction from satellite imagery using deep learning, said method comprising the steps of:
- providing (101) at least two overlapping 2D satellite images,
- providing (102) imaging device parameters for the at least two overlapping 2D satellite images,
- providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view 3D geocoded ground truth data, and
- predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters,
- wherein the prediction of the depth map comprises associating the data of the depth map to geocoded coordinate data.
A method for 3D reconstruction from satellite imagery using deep learning, said method comprising the steps of:
- providing (101) at least two overlapping 2D satellite images,
- providing (102) imaging device parameters for the at least two overlapping 2D satellite images,
- providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view 3D geocoded ground truth data,
- predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters, and
- generating (105) a geocoded 3D surface model based on the predicted depth map.
A method for 3D reconstruction from satellite imagery using deep learning, said method comprising the steps of:
- providing (101) at least two overlapping 2D satellite images,
- providing (102) imaging device parameters for the at least two overlapping 2D satellite images,
- providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view 3D geocoded ground truth data, and
- predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters,
- wherein the trained MLN is arranged to predict depth maps for built-up environments.
A method (300) for training a Machine Learning Network, MLN, for 3D reconstruction from satellite imagery, said method comprising the steps of:
- providing (301) at least one trained Machine Learning Network, MLN, able to predict depth maps, and
- training (302) said MLN on a training set comprising at least one textured or non-textured 3D geocoded 3D surface model for training purposes, wherein multi-view 3D geocoded ground truth data is rendered from said textured or non-textured geocoded 3D surface model for training purposes.
Dependencies are inferred from the claim text.