Point Cloud Normal Vector Appearance Control
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Solution Overview
Problem
Conventional methods for mapping the geometry and attributes of complex objects are labor-intensive, time-consuming, and error-prone, especially in the building industry, where manual surveying and data processing are required, limiting the number of measurable points and necessitating additional field notes for attribute information.
Innovation Solution
A system that uses a scanning lidar to generate a point cloud model, allowing a computer to estimate normal vectors and adjust the appearance of points based on their relationship with the line of sight, enabling improved visualization and data manipulation by controlling transparency, color, or size according to predetermined functions, thereby enhancing the efficiency of data interpretation and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual surveying and data processing methods are used, then attribute information can be recorded, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical surveying methods with automated laser scanning technology. The laser scanner automatically captures point cloud data including geometric and attribute information, eliminating the need for manual measurement and recording while significantly reducing processing time.
Solution Approach 2:
The patent creates a digital point cloud model that serves as a copy of the physical structure. This digital replica contains all geometric and attribute information, allowing multiple analyses and visualizations without requiring additional field measurements or manual data collection.
2Measurement precision
If the number of measurable points is increased, then point cloud accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary organization of point cloud data by creating structured point sets with assigned normal vectors before visualization. This pre-processing step organizes the large volume of point data into manageable groups, reducing the complexity of subsequent processing operations while maintaining high measurement precision.
Solution Approach 2:
The patent transforms point cloud data by calculating and assigning normal vector parameters to each point or point set. This parameter transformation enables more efficient processing and visualization by adding geometric context to the raw coordinate data without increasing the fundamental data volume.
3Manufacturing precision
If normal vectors are calculated for all points, then visualization quality improves, but computational time increases
Solution Approach 1:
The patent divides the point cloud into separate point sets or groups, calculating normal vectors for each segment independently rather than for all points simultaneously. This segmentation reduces the computational burden per processing step while maintaining overall visualization quality across the entire point cloud.
Solution Approach 2:
The patent calculates normal vectors selectively for specific point sets or regions of interest rather than uniformly for all points in the cloud. This partial action approach focuses computational resources on areas requiring detailed visualization while reducing overall processing time for less critical regions.
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 reduces the time and effort required for data processing, increases the accuracy of point cloud representation, and allows for more effective highlighting of object features, improving both human and machine analysis of the data.
Implementation Method 1
a scanning lidar (range finding laser) to quickly and accurately sense the position in three-dimensional space of selected points on the surface of an object
Data Source
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AI summary
A method relating to a point cloud includes defining a line of sight of a point cloud on a display of a computer, estimating a normal vector for at least one point of the plurality of points, and determining the appearance on the display of at least one point of the plurality of points based on the step of estimating a normal vector. One can use the computer to manipulate the point cloud to display a selected view of the scene and calculate the angle between the normal vector of the at least one point and a line of sight. The step of determining the appearance can include determining the transparency, color or size of the point on the display according to the angle between the normal vector and the line of sight.