Depth Image Generation via Multi-Resolution Probability Refinement
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Solution Overview
Problem
Existing methods for generating depth images often result in varying quality due to differences in camera performance, leading to inconsistencies in depth information accuracy.
Innovation Solution
A method involving the reception of an input image, extraction of features, decoding these features for multiple depth resolutions, progressively refining probability distributions for each depth resolution, and generating a target depth image based on the final estimated probability distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a depth camera is used to acquire depth information, then depth images can be obtained, but the quality and accuracy of the depth image varies depending on camera performance
Solution Approach 1:
The depth estimation process is segmented into multiple stages with different resolution levels. The method processes depth information at coarse resolution first, then progressively refines it at finer resolutions. This segmentation allows the system to handle depth estimation in manageable steps, improving both accuracy and consistency by avoiding the need for a single complex high-resolution estimation process.
Solution Approach 2:
The method performs preliminary depth estimation at lower resolutions before proceeding to higher resolutions. By first establishing a coarse depth map and then progressively refining it through multiple resolution levels, the system prepares foundational information that guides subsequent refinement steps, ensuring consistent and accurate depth recovery without requiring all processing to occur at maximum resolution simultaneously.
2Measurement precision
If depth images are processed at high resolution, then accuracy improves, but computational complexity increases
Solution Approach 1:
The processing task is divided into multiple resolution levels, where each level handles a specific portion of the overall complexity. Coarse resolution processing handles global structure and low-frequency depth information, while fine resolution processing focuses on local details and high-frequency variations. This segmentation distributes computational load across multiple manageable stages rather than concentrating all processing at a single high-resolution level.
Solution Approach 2:
The system performs preliminary processing at lower resolutions to establish foundational depth information before proceeding to higher resolutions. This preliminary action reduces the complexity of subsequent processing steps, as the refined processing only needs to adjust and detail the coarse estimation rather than perform complete analysis from scratch at all resolutions simultaneously.
Data Source
AI summary
A method and apparatus for generating a depth image are provided. The apparatus receives an input image, extracts a feature corresponding to the input image, generates features for each depth resolution by decoding the feature using decoders corresponding to different depth resolutions, estimates probability distributions for each depth resolution by progressively refining the features for each depth resolution, and generates a target depth image corresponding to the input image based on a final estimated probability distribution from among the probability distributions for each depth resolution.