Depth Information Supplementing Model for Sparse to Dense Depth Completion
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Solution Overview
Problem
Current depth perception techniques, such as those using radar devices, generate sparse depth maps that lack detailed depth information, making it difficult to achieve accurate and dense depth perception necessary for applications like autonomous driving and computer vision tasks.
Innovation Solution
A depth information processing method utilizing a depth information supplementing model with multiple sub-model units connected in series, where each sub-model unit processes and enhances the intermediate depth information to produce dense depth information, improving prediction accuracy through multi-stage supplementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar device is used for depth perception, then depth information can be obtained, but the depth map is sparse and lacks detailed depth information
Solution Approach 1:
The depth completion model is divided into multiple sub-model units (first sub-model unit, second sub-model unit, third sub-model unit) that process depth information in sequential stages. Each sub-model unit transforms the input depth map at a different processing stage, gradually refining sparse depth information into dense depth information through segmented processing steps.
Solution Approach 2:
Intermediate depth maps serve as mediators between the sparse input depth map and the final dense depth map. The first sub-model unit generates a first intermediate depth map, the second sub-model unit generates a second intermediate depth map, and the third sub-model unit generates a third intermediate depth map that becomes the final output. These intermediate representations facilitate the transformation from sparse to dense depth information.
2Measurement precision
If single-stage depth completion is used, then processing is simple, but prediction accuracy of dense depth information is insufficient
Solution Approach 1:
The depth completion model is divided into multiple sub-model units (first sub-model unit, second sub-model unit, third sub-model unit) that process depth information in sequential stages. Each sub-model unit transforms the input depth map at a different processing stage, gradually refining sparse depth information into dense depth information through segmented processing steps.
Solution Approach 2:
The multi-stage processing ensures continuous refinement of depth information. Each sub-model unit continuously processes the depth map from the previous stage, maintaining and improving depth information quality throughout the processing pipeline rather than performing a single discrete transformation.
Data Source
AI summary
Provided are a depth information processing method, an apparatus, and a storage medium, which relate to the field of image processing and, in particular, to computer vision, deep learning and autonomous driving. A specific implementation includes: determining intermediate depth information of a target scene according to sparse depth information of the target scene by a sub-model unit in a depth information supplementing model; and using intermediate depth information determined by a tail sub-model unit in the depth information supplementing model as dense depth information of the target scene.