3D Spatial Reconstruction from Partial Scans
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Solution Overview
Problem
Legacy systems struggle to accurately capture and represent the layout of a 3D space, particularly when objects occlude boundaries and partial scans result in incomplete data.
Innovation Solution
The system analyzes a point cloud or mesh from a 3D scan to identify walls, floors, and other boundaries, distinguishing them from intervening objects, and generates a 3D metaverse with metadata that includes physical dimensions of the space and recognized objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If legacy systems scan a room to present environmental information, then basic spatial data is obtained, but the information is limited and lacks value for understanding room contents
Solution Approach 1:
The system segments the scanned space into distinct functional zones (e.g., living area, dining area, bedroom) by analyzing spatial relationships, object distributions, and architectural features. This segmentation enables comprehensive information extraction without requiring a single complex monolithic scanner, as each zone can be processed independently to identify contents and characteristics.
Solution Approach 2:
The system transitions from traditional 2D floor plans to 3D spatial modeling with height and depth information. By creating three-dimensional representations of the space, the system captures vertical elements (ceilings, wall-mounted objects) and depth relationships that legacy 2D systems miss, significantly improving information completeness about room contents and layout.
2Reliability
If objects are present in the scanned space, then rich environmental data is captured, but occlusions cause boundaries to be hidden and data becomes incomplete
Solution Approach 1:
The system performs preliminary multi-angle scanning or uses depth sensors (LiDAR, time-of-flight cameras) to capture spatial information before final boundary rendering. By pre-capturing data from multiple perspectives and depth layers, the system establishes a complete point cloud or mesh that reveals boundaries even when occluded in any single view, ensuring accurate boundary detection despite object presence.
Solution Approach 2:
The system introduces computational algorithms as intermediaries that process raw scan data to infer hidden boundaries. These algorithms analyze patterns in the captured data, extrapolate occluded regions based on visible geometry and contextual clues, and reconstruct complete boundary representations even when direct observation is blocked by objects in the space.
3Productivity
If partial scans are performed to reduce scanning time, then speed is improved, but the resulting data is incomplete and inaccurate
Solution Approach 1:
The system performs partial scans of key areas first to establish the basic spatial framework and major boundaries, then selectively scans additional regions only when needed for complete accuracy. This approach achieves acceptable precision quickly for most applications, while allowing optional deeper scanning of specific zones to improve measurements only where necessary, balancing speed and accuracy dynamically.
Solution Approach 2:
The system uses feedback loops where initial scan results are analyzed to determine if the captured data meets accuracy thresholds. If precision is insufficient in certain areas, the system automatically triggers additional scanning of those specific regions. This feedback-driven approach ensures measurement precision is maintained while minimizing unnecessary scanning, optimizing the balance between productivity and accuracy.
Data Source
AI summary
Embodiments herein may relate to generating, based on a three-dimensional (3D) graphical representation of a 3D space, a two-dimensional (2D) image that includes respective indications of respective locations of one or more objects in the 3D space. The 2D image may then be displayed to a user that provides user input related to selection of an object of the one or more objects. The graphical representation of the object in the 2D image may then be altered based on the user input. Other embodiments may be described and/or claimed.