Monocular Depth Estimation with Plane-Aware Confidence Fusion
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Solution Overview
Problem
Monocular depth estimation faces challenges due to scale ambiguity and the lack of effective utilization of high regularity in real-world 3D scenes, leading to inaccurate depth predictions in existing systems.
Innovation Solution
A neural network architecture comprising a pixel-based depth estimator and a seed-pixel-based depth estimator, utilizing a plane coefficient detector and an offset-vector-field detector, with a fusion unit to combine depth estimates, leveraging the piecewise planarity prior to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pixel-based depth estimation is used independently for each pixel, then computational simplicity is maintained, but depth map accuracy deteriorates due to ignoring spatial regularity
Solution Approach 1:
The image is segmented into multiple planes, with each plane representing a region of 3D points located on a common plane. The depth estimation process is then performed separately for each plane rather than for each individual pixel, allowing the system to exploit spatial regularity within planes while maintaining computational efficiency through parallel processing of plane-level data.
Solution Approach 2:
The approach transitions from pixel-level (2D image space) to plane-level (3D scene space) processing by introducing plane coefficients as an intermediate representation. This dimensional change allows the system to capture spatial regularity and piecewise smoothness properties that are invisible at the pixel level, thereby improving depth accuracy without proportionally increasing computational complexity.
2Measurement precision
If plane-based depth estimation is used, then spatial regularity is exploited, but depth accuracy deteriorates due to inability to handle depth discontinuities
Solution Approach 1:
The scene is explicitly segmented into multiple discrete planes, where each plane corresponds to a region with consistent depth characteristics. This segmentation allows the system to handle depth discontinuities by treating different planes separately, with each plane's depth estimation being independent of others, thereby preserving spatial regularity within planes while accurately representing discontinuities between planes.
Solution Approach 2:
The system dynamically determines the number, position, and orientation of planes based on the input image content rather than using a fixed plane configuration. This dynamic adaptation allows the plane-based model to flexibly handle various scene complexities, including scenes with many depth discontinuities or irregular structures, by adjusting the plane segmentation to match the actual scene geometry.
3Device complexity
If monocular depth estimation is performed, then system complexity is reduced compared to stereo systems, but depth accuracy deteriorates due to scale ambiguity
Solution Approach 1:
The system performs preliminary plane detection and coefficient estimation before final depth calculation. By first identifying the planar structures in the scene and their geometric parameters (plane coefficients), the system establishes a structural framework that constrains the subsequent depth estimation process, thereby reducing the impact of scale ambiguity inherent in monocular systems.
Solution Approach 2:
The approach changes the parameter representation from direct pixel-wise depth values to plane coefficients (normal vectors and distances from origin). This parameter transformation allows the system to exploit the geometric constraints of planar surfaces, providing additional structural information that compensates for the scale ambiguity problem in monocular depth estimation while maintaining computational efficiency.
Data Source
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AI summary
Systems and methods estimate depth of 3D points corresponding to pixels in an image using a trained neural network architecture including a pixel-based depth estimator (4) and a seed-pixel-based depth estimator (6). The pixel-based depth estimator (4) predicts plane coefficients of respective pixels in the image, and generates a first depth estimate by converting the plane coefficients to depth values. The seed-pixel-based depth estimator (6) predicts offset vectors between target pixels and seed pixels on planes in the scene represented by the image, resamples the plane coefficients produced by the plane coefficient detector, and generates a second depth estimate by converting the resampled plane coefficients to depth values. The first and second estimates are fused to produce a final depth estimate, notably by taking a weighted average based on a parameter quantifying the confidence that each pixel is on the same plane as its seed pixel.