Viewpoint Path Smoothing for Handheld Multiview Image Navigation
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Solution Overview
Problem
Images captured by handheld devices, such as mobile phones, often suffer from significant distortions due to non-closed loop capturing and vertical movement, leading to challenges in presenting the data effectively.
Innovation Solution
A method involving determining first and second coordinate points, converting them between Cartesian and polar coordinate spaces, and fitting polynomial curves to smooth the trajectory, followed by transforming images to align with the smoothed trajectory, using motion data from inertial measurement units and depth sensors to enhance image data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If images are captured by handheld device moving along arbitrary path, then ease of operation is improved, but image distortion and navigation accuracy deteriorate
Solution Approach 1:
The patent transforms coordinate parameters from Cartesian to polar coordinate system, where the radial distance and angular position separately capture the camera's position information. This parameter transformation allows the trajectory to be represented more naturally in polar coordinates, improving the accuracy of viewpoint location determination while maintaining ease of handheld operation.
Solution Approach 2:
The patent introduces polynomial curves as intermediary mathematical models to fit and smooth the raw trajectory data obtained from sensors. These polynomial curves serve as mediators between the noisy sensor measurements and the final viewpoint determination, filtering out disturbances while preserving the essential motion path information.
2Stability of the object's composition
If polynomial curve fitting is applied to smooth trajectory, then navigation seamlessness is improved, but computational complexity increases
Solution Approach 1:
The patent applies polynomial curve fitting selectively to specific portions of the trajectory data that require smoothing, rather than processing all data points uniformly. By identifying key segments of the trajectory that benefit from smoothing and applying polynomial fitting only to those segments, the system achieves navigation seamlessness while limiting the overall computational burden.
3Measurement precision
If coordinate transformation from Cartesian to polar is performed, then trajectory representation accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent performs coordinate transformation from Cartesian to polar coordinates as a preliminary step in the trajectory processing pipeline, converting all position data to polar coordinates before trajectory fitting and smoothing operations. This preliminary transformation simplifies subsequent processing steps and enables more accurate viewpoint determination, as the polar coordinate system naturally represents radial and angular motion patterns.
Data Source
AI summary
A set of images may be captured by a camera as the camera moves along a path through space around an object. Then, a smoothed function (e.g., a polynomial) may be fitted to the translational and/or rotational position in space. For example, positions in a Cartesian coordinates pace may be determined for the images. The positions may then be transformed to a polar coordinate space, in which a trajectory along the points may be determined, and the trajectory transformed back into the Cartesian space. Similarly, the rotational position of the images may be smoothed, for instance by fitting a loss function. Finally, one or more images may be transformed to more closely align a viewpoint of the image with the fitted translational and/or rotational positions.