Point Cloud Attribute Estimation with Reliability-Based Display Control
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Solution Overview
Problem
Conventional techniques for estimating attributes from point cloud data struggle with display control, particularly in evaluating the reliability of estimation results, leading to difficulties in determining the existence of input points and managing visibility effectively.
Innovation Solution
An estimation device that combines attention points with observation points to generate second point cloud data, using neural networks for attribute estimation and reliability assessment, and performs display control based on the reliability of the estimation results to ensure accurate representation and visibility management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional attribute estimation techniques are used, then attribute classification is performed, but reliability evaluation and display control cannot be performed
Solution Approach 1:
The patent divides the estimation system into two separate neural networks: an attribute estimation network and a reliability estimation network. Each network specializes in a specific function, allowing the system to evaluate both attribute classification and reliability independently. This segmentation enables reliability evaluation without significantly increasing overall system complexity.
Solution Approach 2:
The reliability estimation network uses the same input data (point cloud features) as the attribute estimation network, making it a multi-functional system. Both networks process the same input to provide different outputs (attribute classification and reliability assessment), improving system efficiency while maintaining manageable complexity.
2Measurement precision
If maximum value attribute is adopted for representation, then estimation output is simplified, but erroneous estimation cannot be detected
Solution Approach 1:
The reliability estimation network acts as an intermediary that assesses the quality of the attribute estimation output. By introducing this intermediate reliability evaluation step, the system can detect erroneous estimations without losing the original attribute classification information, thus preventing information loss while improving measurement precision.
Solution Approach 2:
The system implements feedback by using the reliability estimation results to control the display and selection of attribute representations. When reliability is low, the system can indicate uncertainty or exclude the estimation from final representation, creating a feedback loop that improves overall estimation accuracy while preserving information about estimation confidence.
3Manufacturing precision
If manual editing of point cloud data is performed, then data quality is improved, but processing time increases significantly
Solution Approach 1:
The system performs self-service by automatically evaluating the reliability of attribute estimations and controlling display accordingly, eliminating the need for manual data editing. The neural networks autonomously identify and flag low-reliability estimations, maintaining data quality while dramatically improving processing speed compared to manual methods.
Solution Approach 2:
The patent replaces the mechanical manual editing process with an automated computational system using neural networks. This substitution maintains or improves data quality through intelligent reliability assessment while eliminating the time-consuming nature of manual editing, thus resolving the contradiction between precision and productivity.
4Loss of information
If all points are displayed in point cloud data, then complete information is provided, but visibility management becomes difficult
Solution Approach 1:
The system uses visual differentiation (analogous to color changes) by controlling the display based on reliability estimates. Points with high reliability can be displayed with full visibility while low-reliability points are either excluded or displayed with different visual indicators, maintaining information completeness while greatly improving visibility management and ease of operation.
Data Source
AI summary
According to an embodiment, an estimation device includes one or more processors configured to: generate, from first point cloud data, second point cloud data obtained by combining an attention point and observation points; estimate an attribute of the attention point by an attribute indicated by an estimation result label having a higher belonging probability among belonging probabilities output from an attribute estimation neural network; estimate reliability of the estimation result label by a reliability estimation neural network; and display, on a display device, first display information generated by performing rendering on an object including an attention point whose attribute is estimated by an attribute of the estimation result label whose reliability is higher than a first threshold, and generated by not performing rendering on an object including an attention point whose attribute is estimated by an attribute of the estimation result label whose reliability is the first threshold or less.