Point Cloud Attribute Estimation Using Attention and Observation Points
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Solution Overview
Problem
Current methods for managing and maintaining plant and social infrastructure require manual editing of point cloud data, which is time-consuming and inefficient, necessitating automated techniques to estimate attributes from point cloud data and color information.
Innovation Solution
An estimation apparatus that acquires point cloud data, selects attention points, combines them with observation points to generate second point cloud data, and uses deep learning to estimate attributes by calculating belonging probabilities, thereby improving estimation accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual editing of point cloud data is performed, then data quality can be ensured, but processing time increases significantly
Solution Approach 1:
The patent replaces manual mechanical editing operations with an automated deep learning-based estimation system. The system uses neural networks to automatically estimate attributes of attention points by processing point cloud data and color information, eliminating the need for manual data editing while maintaining high accuracy in attribute estimation.
2Measurement precision
If the entire shape is considered without clearly defining selection range, then estimation accuracy improves, but calculation complexity increases
Solution Approach 1:
The patent segments the point cloud data by identifying specific attention points that require attribute estimation, rather than processing the entire dataset uniformly. The system selectively processes only relevant portions of the data (attention points and their associated observation points) while considering the global shape context, thus balancing accuracy with computational efficiency.
Solution Approach 2:
The patent applies local quality by assigning different processing priorities to different regions of the point cloud data. Attention points receive focused computational resources for detailed attribute estimation, while other regions are processed more efficiently. This allows the system to consider the entire shape context without uniformly processing all data points at high computational cost.
3Productivity
If automated attribute estimation is implemented, then productivity increases, but estimation accuracy may deteriorate
Solution Approach 1:
The patent introduces deep learning models as intermediary components between the raw point cloud data and the final attribute estimation results. The neural networks serve as intelligent mediators that automatically learn complex patterns and relationships from the data, enabling accurate attribute estimation without manual intervention. This intermediary layer maintains high accuracy while achieving full automation and improved productivity.
Data Source
AI summary
An estimation apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: acquire first point cloud data; generate, from the first point cloud data, second point cloud data in which an attention point and at least one observation point are combined, the attention point gaining attention as a target of attribute estimation; and estimate an attribute of the attention point by calculating, for each attribute, a belonging probability of belonging to the attribute by using the second point cloud data.