Monocular Depth Estimation with Pose-Guided Pixel Matching
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Solution Overview
Problem
Existing deep learning approaches for estimating depth from monocular images lack accuracy due to the difficulty in estimating depth from a single 2D image without additional information.
Innovation Solution
Utilize geometry-based techniques to estimate relative camera poses and define a search space between image pairs, predicting likelihoods of correspondences using neural networks, and applying optical flow estimation to filter and triangulate depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If monocular depth regression is used to estimate depth from a single 2D image, then the process can be performed independently, but the accuracy and robustness of depth estimation deteriorates
Solution Approach 1:
The patent merges pose estimation and depth estimation into a unified framework where pose information from multiple images is combined with depth regression to produce more accurate depth maps. The pose network and depth network work together synergistically, with pose estimates serving as constraints and priors for depth estimation, thereby improving accuracy while maintaining operational efficiency.
Solution Approach 2:
The system implements feedback mechanisms where depth estimates from initial processing are used to refine pose estimates, which in turn refine depth estimates further. This iterative feedback loop continues until convergence, with each iteration improving the accuracy of depth estimation while maintaining computational efficiency through progressive refinement.
2Reliability
If geometry-based techniques are used to estimate relative camera poses, then the robustness of depth estimation improves, but the computational complexity increases
Solution Approach 1:
The patent segments the complex computation into separate modules: a pose estimation module that processes image pairs to extract relative camera poses, a depth estimation module that uses pose information and monocular images to generate depth maps, and a refinement module that iteratively improves results. This segmentation allows each module to be optimized independently, improving robustness while managing computational complexity through modular architecture.
Solution Approach 2:
The system performs preliminary pose estimation using geometry-based techniques on image pairs to establish a framework before conducting detailed depth estimation. This preliminary action reduces the search space and provides constraints for subsequent depth regression, thereby improving robustness while reducing the overall computational burden of the depth estimation process.
3Measurement precision
If relative camera pose estimation is performed to define search space, then the accuracy of depth estimation improves, but the processing time increases
Solution Approach 1:
The patent applies partial action by performing pose estimation only on a subset of image pairs and using these partial pose estimates to define search spaces for depth estimation. Rather than computing poses for all possible image combinations, the system strategically selects representative pairs that provide sufficient constraints, thereby improving depth accuracy while reducing processing time through selective computation.
Solution Approach 2:
The system performs preliminary pose estimation on a subset of images to establish a framework that guides subsequent depth estimation. These preliminary pose estimates are used to define search spaces and provide geometric constraints, enabling accurate depth estimation without requiring exhaustive processing of all image pairs, thus reducing overall processing time while maintaining precision.
Data Source
AI summary
In various examples, methods and systems are provided for estimating depth values for images (e.g., from a monocular sequence). Disclosed approaches may define a search space of potential pixel matches between two images using one or more depth hypothesis planes based at least on a camera pose associated with one or more cameras used to generate the images. A machine learning model(s) may use this search space to predict likelihoods of correspondence between one or more pixels in the images. The predicted likelihoods may be used to compute depth values for one or more of the images. The predicted depth values may be transmitted and used by a machine to perform one or more operations.