Monocular Depth Recovery via Scene Structure Decoupling
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Solution Overview
Problem
Depth recovery of monocular images is challenging due to the uncertainty of depth scale, leading to complex solutions that require large amounts of data and training time with low precision in existing depth estimation schemes.
Innovation Solution
The method involves performing feature extraction, decoupling to obtain a scene structure graph, gradient sensing to enhance features, and depth estimation using a region-enhanced feature image, decoupling depth estimation into scene structure and depth estimation to accelerate convergence and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing depth estimation schemes simultaneously solve scene structure and depth scale estimation, then depth recovery can be achieved, but the solution process becomes complex, requiring large amounts of data and training time with low precision
Solution Approach 1:
The patent applies segmentation by decoupling the depth estimation problem into two separate modules: scene structure estimation and depth scale estimation. The scene structure estimation module first processes the input image to obtain scene structure information, which is then fed into the depth scale estimation module to generate the final depth map. This separation simplifies each individual module's task, improving training efficiency and precision while reducing overall system complexity.
2Measurement precision
If existing depth estimation schemes simultaneously solve scene structure and depth scale estimation, then depth recovery can be achieved, but large amounts of data and training time are required
Solution Approach 1:
By segmenting the estimation task into two specialized modules, each module can be trained more efficiently on smaller datasets. The scene structure estimation module focuses solely on geometric relationships, while the depth scale estimation module focuses on scale calibration, reducing the overall training time and data requirements compared to a unified approach.
Solution Approach 2:
The scene structure estimation is performed as a preliminary step before depth scale estimation. By pre-computing the scene structure information and using it as input for the subsequent depth estimation, the system reduces the complexity of the second stage training, thereby reducing total training time and data requirements.
3Device complexity
If monocular depth recovery is performed without decoupling, then the process is simpler, but precision is low and convergence is slow
Solution Approach 1:
The patent implements segmentation by creating two specialized estimation modules that process different aspects of depth recovery separately. This segmentation improves precision and convergence speed by allowing each module to specialize in specific features, overcoming the limitations of a single undifferentiated model while maintaining reasonable process complexity.
Data Source
AI summary
A depth recovery method includes: performing feature extraction on a monocular image to obtain a feature image of the monocular image; decoupling the feature image to obtain a scene structure graph of the feature image; performing gradient sensing on the feature image and the scene structure graph to obtain a region-enhanced feature image; and performing depth estimation according to the region-enhanced feature image to obtain a depth image of the monocular image.