3D Scene Alignment via Depth Map Copying and Feedback
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Solution Overview
Problem
Current methods for capturing and aligning 3D scenes are limited by the need for complex hardware and require manual input, making them inaccessible to non-technical users and prone to errors due to incomplete or low-quality data.
Innovation Solution
A method using 3D capture devices to compute feature data from multiple perspectives, applying coordinate transformations to align scenes, and implementing a hole-filling process to address incomplete data, allowing users to visually or algorithmically fill gaps, with feedback mechanisms to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors are used to capture 3D information, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses depth maps as a simplified representation (copy) of the actual 3D scene data captured by Kinect sensors. Instead of directly processing complex point cloud data from LIDAR-like sensors, the system creates and processes depth map images that contain the essential 3D information in a more manageable format, reducing hardware complexity while maintaining measurement precision
Solution Approach 2:
The patent replaces complex mechanical LIDAR scanning systems with optical-based Kinect sensors that use structured light and time-of-flight measurement. This substitution allows for 3D capture without the mechanical complexity of traditional LIDAR systems, achieving comparable measurement precision through different physical principles
2Manufacturing precision
If ICP algorithm is used for alignment, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent performs preliminary actions by automatically detecting overlapping regions and computing initial alignment transformations before applying the full ICP algorithm. This preliminary processing reduces the computational burden and complexity that users would otherwise need to manage manually, while still achieving high alignment precision through the subsequent ICP refinement
Solution Approach 2:
The system performs self-service by automatically detecting features, computing correspondences, and executing alignment without requiring user intervention. The algorithm autonomously identifies overlapping regions, selects matching features, and applies transformations, eliminating the need for users to manually input alignment parameters while maintaining high precision
3Quantity of substance
If multiple 3D scenes are captured from different locations, then completeness is improved, but loss of information increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously evaluates alignment quality and detects holes in the composite scene. Based on this feedback, it automatically identifies regions that need additional capture and guides the capture process to target those specific areas, ensuring that completeness is improved without sacrificing data quality through indiscriminate over-capture
Solution Approach 2:
The patent applies local quality by processing and evaluating each captured scene individually, identifying specific regions with poor quality or holes, and then targeting those local areas for additional capture or improvement. This approach ensures that information loss is minimized by focusing resources on specific problematic regions rather than uniformly processing all data
4Measurement precision
If manual alignment input is required, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically computing alignment transformations through feature detection and matching. It autonomously identifies corresponding features across scenes, computes transformation matrices, and applies alignments without requiring users to manually input alignment data, making the process accessible to non-technical users while maintaining precision through algorithmic accuracy
Data Source
AI summary
The capture and alignment of multiple 3D scenes is disclosed. Three dimensional capture device data from different locations is received thereby allowing for different perspectives of 3D scenes. An algorithm uses the data to determine potential alignments between different 3D scenes via coordinate transformations. Potential alignments are evaluated for quality and subsequently aligned subject to the existence of sufficiently high relative or absolute quality. A global alignment of all or most of the input 3D scenes into a single coordinate frame may be achieved. The presentation of areas around a particular hole or holes takes place thereby allowing the user to capture the requisite 3D scene containing areas within the hole or holes as well as part of the surrounding area using, for example, the 3D capture device. The new 3D captured scene is aligned with existing 3D scenes and/or 3D composite scenes.