Depth image generation method, apparatus, and storage medium and electronic device

A depth image generation apparatus, the apparatus comprising:

• at least one memory configured to store computer program code;
• and
• at least one processor configured to read the computer program code and operate as instructed by the computer program code, the computer program code comprising:
• image acquisition code configured to cause the at least one processor to acquire a plurality of target images, the plurality of target images being obtained respectively by photographing a target object from different views;
• convolution processing code configured to cause the at least one processor to perform multi-stage convolution processing on the plurality of target images through a plurality of convolutional layers in a convolution model to obtain feature map sets respectively outputted by the plurality of convolutional layers, each feature map set comprising feature maps corresponding to the plurality of target images;
• view aggregation code configured to cause the at least one processor to perform view aggregation on a plurality of feature maps in each feature map set respectively to obtain an aggregated feature corresponding to each feature map set;
• and
• feature fusion code configured to cause the at least one processor to perform fusion processing on the plurality of obtained aggregated features to obtain a depth image,
• wherein the view aggregation code further comprises:
• image determining code configured to cause the at least one processor to regard one of the target images as a reference image, and regard other target images in the plurality of target images as a first image;
• and
• wherein the view aggregation code is further configured to cause the at least one processor to perform the following processing on any feature map set:
• determine, in the feature map set, a reference feature map corresponding to the reference image and a first feature map corresponding to the first image;
• perform, according to a difference between photographing views of the first image and the reference image, view conversion on the first feature map to obtain a second feature map after conversion;
• and
• perform fusion processing on the reference feature map and the second feature map to obtain the aggregated feature.

A non-transitory computer-readable storage medium, storing computer program code that when executed by at least one processor causes the at least one processor to:

• acquire a plurality of target images, the plurality of target images being obtained respectively by photographing a target object from different views;
• perform multi-stage convolution processing on the plurality of target images through a plurality of convolutional layers in a convolution model to obtain feature map sets respectively outputted by the plurality of convolutional layers, each feature map set comprising feature maps corresponding to the plurality of target images;
• perform view aggregation on a plurality of feature maps in each feature map set respectively to obtain an aggregated feature corresponding to each feature map set;
• and
• perform fusion processing on the plurality of obtained aggregated features to obtain a depth image,
• wherein the perform view aggregation on a plurality of feature maps in each feature map set respectively to obtain an aggregated feature corresponding to each feature map set comprises:
• regarding any one of the target images as a reference image, and regarding other target images in the plurality of target images as a first image;
• performing the following processing on a feature map set:
• determining, in the feature map set, a reference feature map corresponding to the reference image and a first feature map corresponding to the first image.