3D Scan Alignment Using Inactive Cluster Merging
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Solution Overview
Problem
Conventional 3-D scanners face issues with alignment failures during the scanning process, leading to increased scan time and user inconvenience due to the need for repeated scans and difficulty in alternately confirming the object and display screen, which hinders the rapid generation of high-quality 3-D models.
Innovation Solution
A data processing method and apparatus that aligns scan shots with pre-formed inactive clusters and selectively merges active and inactive clusters based on alignment success, allowing for continuous scanning and minimizing the number of generated clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alignment failure causes the 3-D modeling process to stop, then the model completion quality is maintained, but the scan time increases and user convenience deteriorates
Solution Approach 1:
The system performs preliminary alignment attempts with multiple inactive clusters before finalizing the active cluster. When alignment fails with the current active cluster, the system has pre-prepared inactive clusters that can be activated and merged, allowing continuous operation without stopping for re-scanning.
Solution Approach 2:
When alignment fails with the current active cluster, the system discards the failed alignment attempt and recovers by activating a pre-formed inactive cluster. This recovered cluster is then merged with the active cluster, preserving progress and avoiding complete restart.
2Measurement precision
If the user alternately confirms the object and display screen, then alignment accuracy is improved, but the user concentration is dispersed and scan time increases
Solution Approach 1:
The system performs self-alignment by automatically comparing the scan shot with multiple inactive clusters and selecting the best match. This eliminates the need for user intervention to confirm alignment, allowing the system to maintain focus on the scanning task without分散ing user concentration.
Solution Approach 2:
The system provides feedback by displaying alignment results and allowing users to review the generated 3-D model. This feedback mechanism ensures alignment accuracy while reducing the need for repeated alternating checks between object and display screen.
3Adaptability or versatility
If multiple clusters are generated during scanning, then alignment flexibility is improved, but resource usage increases and operation complexity increases
Solution Approach 1:
The system segments the scanning process into discrete clusters, where each cluster represents a coherent group of scan shots. This segmentation allows flexible recombination of clusters during alignment while simplifying the overall operation by providing clear, manageable units rather than managing individual scan shots independently.
Solution Approach 2:
The system merges inactive clusters with the active cluster when alignment is successful. This merging operation consolidates multiple clusters into a unified structure, reducing the number of separate data structures to manage while preserving the flexibility to combine different cluster configurations.
Data Source
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AI summary
A data processing method includes obtaining a scan shot representing an object, aligning the scan shot with a pre-formed at least one inactive cluster, and selectively merging an active cluster including the scan shot and the inactive cluster based on whether the scan shot is aligned with the inactive cluster.