Panoramic depth map generation method, model training method, electronic device, and unmanned vehicle

A method for training a depth estimation model, comprising:

• grouping sample images involving different orientations in a sample scene based on training samples to form at least two sample image groups, the sample images in each of the at least two sample image groups covering a panoramic field of view of the sample scene;
• performing feature combination on sample image feature volumes of each of the sample image groups to obtain at least two sample panoramic feature volumes, each of the sample image feature volumes representing three-dimensional features of one of the sample images;
• performing correlation processing on every two of the at least two sample panoramic feature volumes to obtain at least one sample correlation volume;
• performing panoramic depth estimation based on an initial depth map and the at least one sample correlation volume to obtain a predicted panoramic depth map for the sample scene;
• determining loss information of the depth estimation model according to the predicted panoramic depth map;
• adjusting network parameters of the depth estimation model iteratively according to the loss information until the loss information satisfies an iteration stop condition, and
• determining the network parameters obtained when the loss information satisfies the iteration stop condition as the trained depth estimation model.

An electronic device, comprising:

• at least one processor;
• and
• at least one memory for storing at least one program,
• wherein, the at least one processor, when executing the at least one program, is configured to:
• group target images involving different orientations in a target scene to form at least two image groups, the target images in each of the at least two image groups covering a panoramic field of view of the target scene;
• perform feature combination on target image feature volumes of each of the image groups to obtain at least two target panoramic feature volumes, each of the target image feature volumes representing three-dimensional stereoscopic features of one of the target images;
• perform correlation processing on every two of the at least two target panoramic feature volumes to obtain at least one target correlation volume;
• and
• perform panoramic depth estimation based on an initial depth map and the at least one target correlation volume to obtain the target panoramic depth map for the target scene.