3D Data Pixel Alignment via Surface Estimation
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Solution Overview
Problem
Existing methods for aligning 3D data from LiDAR with image data struggle to establish an accurate correspondence between points and pixels due to differences in coarseness and arrangement, limiting rigorous analysis.
Innovation Solution
A measurement apparatus and system that includes an alignment unit, a pixel selection unit, and a pixel point association unit to align image and 3D data, selecting neighboring points to estimate a surface and calculate the position of target pixels within the 3D space, enabling precise association between image and 3D data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If alignment is performed between 3D data and image data using existing methods, then alignment can be achieved, but accurate correspondence cannot be established due to differences in coarseness and arrangement of points and pixels
Solution Approach 1:
The patent segments the correspondence establishment process into multiple stages: first performing rough alignment between 3D data and image data, then selectively establishing correspondence only for pixels that actually overlap with 3D points. This segmentation allows the system to handle the fundamental mismatch between point-based 3D data and pixel-based images by processing them in distinct phases rather than attempting direct one-to-one mapping.
Solution Approach 2:
The patent applies local quality by treating different pixels differently based on their actual correspondence with 3D points. Instead of assuming uniform correspondence across all pixels, the system identifies and processes only those pixels that have actual 3D point overlaps, giving each pixel group localized treatment according to its specific spatial relationship with the 3D data. This resolves the contradiction by making correspondence accuracy local rather than global.
2Ease of operation
If one-to-one correspondence is attempted between 3D points and image pixels, then direct mapping can be performed, but quantitative alignment cannot be ensured due to coarseness differences
Solution Approach 1:
The patent transitions from attempting two-dimensional pixel-to-point mapping to a three-dimensional approach by calculating actual spatial distances between pixels and 3D points in 3D space. By introducing the third dimension (depth/Z-coordinate) into the correspondence calculation, the system can accurately determine which pixels actually overlap with 3D points, resolving the quantitative alignment issue that plagues 2D mapping approaches.
Solution Approach 2:
The patent uses the concept of spatial distance calculation as an intermediary to bridge the gap between 3D points and 2D pixels. Rather than directly mapping pixels to points, the system calculates distances in 3D space as an intermediate step to determine correspondence. This intermediary distance calculation resolves the precision issue by providing an objective quantitative measure for establishing accurate pixel-point relationships.
3Area of stationary object
If all pixels are processed for correspondence establishment, then complete coverage is achieved, but processing efficiency decreases due to unnecessary calculations
Solution Approach 1:
The patent extracts and processes only the relevant subset of pixels that actually correspond to 3D points, rather than processing all pixels in the image. By identifying and extracting only those pixels with actual 3D point overlaps, the system eliminates unnecessary processing of pixels that would not contribute to the final correspondence, thereby maintaining complete coverage of relevant areas while significantly improving processing efficiency.
Solution Approach 2:
The patent applies partial action by performing correspondence establishment only for pixels that have actual 3D point overlaps, rather than attempting to process all pixels. This partial processing approach is sufficient to achieve the goal of accurate correspondence establishment while avoiding the waste of computational resources on pixels that would not contribute meaningful information, thus resolving the efficiency-coverage contradiction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for the construction of an accurate correspondence relation between 3D data points and image pixels, facilitating precise alignment and analysis.
Implementation Method 1
select, from a plurality of point data items of the 3D data included in the data after the alignment, three or more point data items near the target pixel as neighboring point data, estimate one surface based on the neighboring point data, and perform association for calculating a position of the target pixel on the one surface in a space which the plurality of point data items belong to
Data Source
AI summary
An object is to construct an accurate correspondence relation between points included in 3D data and pixels that form an image. An alignment unit receives image data acquired by capturing an image of an object, and 3D data of the object, performs alignment between the image data and the 3D data, and outputs data after the alignment. A pixel selection unit selects a target pixel from a plurality of pixels included in the image data. The pixel point association unit selects, from a plurality of point data items of 3D data included in the data after the alignment, three or more point data items near the target pixel as neighboring point data, estimates one surface based on the neighboring point data, and performs association for calculating a position of the target pixel on the one surface in a space which the plurality of point data items belong to.