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US 12,694,564 B2Granted
Depth estimation method and apparatus, method and apparatus for training depth estimation model, and system
AssigneeYinwang Intelligent Technologies Co., Ltd.
InventorsQi Cao, Di Zhang, Shuzhan Bi
Life of this patent
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Abstract
In a depth estimation method, a depth estimation apparatus obtains a first color image, and inputs the first color image into a first depth estimation model to obtain a first intermediate depth image. The depth estimation apparatus then inputs the first color image and the first intermediate depth image into a second depth estimation model to obtain a first target depth image. The second depth estimation model is obtained through training based on a color image and a target depth image corresponding to the color image, and the first depth estimation model is obtained through training based on the color image and an intermediate depth image corresponding to the color image.
Claims17
A depth estimation method performed by a depth estimation device, comprising:
- obtaining a first color image;
- inputting the first color image into a first depth estimation model, to obtain a first intermediate depth image, wherein the first depth estimation model is obtained through training based on a training color image and an intermediate depth image corresponding to the training color image;
- and inputting the first color image and the first intermediate depth image into a second depth estimation model, to obtain a first target depth image, wherein the second depth estimation model is obtained through training based on the training color image and a target depth image corresponding to the training color image, wherein the first depth estimation model and the second depth estimation model are neural network models, wherein the first target depth image is a refined image obtained based on the first intermediate depth image, wherein inputting the first color image and the first intermediate depth image into the second depth estimation model to obtain the first target depth image comprises:
- obtaining a common edge image based on the first color image and the first intermediate depth image, wherein the common edge image represents a common edge part of the first color image and the first intermediate depth image;
- and obtaining the first target depth image based on the common edge image and the first intermediate depth image.
2 more elements
A method for training a depth estimation model, comprising:
- processing a color image by using a first depth estimation model, to obtain an intermediate depth image of the color image;
- processing the color image and the intermediate depth image of the color image by using a second depth estimation model, to obtain a target depth image of the color image;
- training the first depth estimation model based on the color image and the intermediate depth image of the color image, to obtain an updated first depth estimation model;
- and training the second depth estimation model based on the color image and the target depth image of the color image, to obtain an updated second depth estimation model, wherein the first depth estimation model and the second depth estimation model are neural networks, wherein the target depth image is a refined image obtained based on the intermediate depth image, wherein the updated second depth estimation model is configured to:
- obtain a common edge image based on the first color image and the first intermediate depth image, wherein the common edge image represents a common edge part of the first color image and the first intermediate depth image;
- and obtain the first target depth image based on the common edge image and the first intermediate depth image.
3 more elements
A depth estimation apparatus comprising:
- a data interface;
- a memory storing executable instructions;
- and a processor configured to execute the executable instructions to perform operations of:
- obtaining, through the data interface, a first color image;
- inputting the first color image into a first depth estimation model to obtain a first intermediate depth image, wherein the first depth estimation model is obtained through training based on a training color image and an intermediate depth image corresponding to the training color image;
- and inputting the first color image and the first intermediate depth image into a second depth estimation model to obtain a first target depth image, wherein the second depth estimation model is obtained through training based on the training color image and a target depth image corresponding to the color image, wherein the first depth estimation model and the second depth estimation model are neural network models, wherein the first target depth image is a refined image obtained based on the first intermediate depth image, wherein inputting the first color image and the first intermediate depth image into the second depth estimation model to obtain the first target depth image comprises:
- obtaining a common edge image based on the first color image and the first intermediate depth image, wherein the common edge image represents a common edge part of the first color image and the first intermediate depth image;
- and obtaining the first target depth image based on the common edge image and the first intermediate depth image.
5 more elements
Dependencies are inferred from the claim text.