3D Model Segmentation for Real-Time Dense Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques face challenges in constructing a real-time, dense three-dimensional model of a space using image data from a moving capture device, particularly with unpredictable motion and large amounts of data, leading to computational inefficiencies and inaccuracies.
Innovation Solution
An apparatus and method that segment a three-dimensional model into active and inactive portions based on model properties, using active portions to update the model and align them with inactive portions over time, incorporating a registration engine to estimate the pose of the capture device and perform non-rigid deformations for alignment, enabling real-time dense mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If dense mapping techniques are used to generate three-dimensional representations, then the detail and completeness of the model is improved, but the computational time and processing requirements increase significantly
Solution Approach 1:
The three-dimensional model is divided into multiple portions or regions, allowing the system to process and update only specific segments rather than the entire model. This segmentation enables real-time updates by focusing computational resources on relevant portions while maintaining overall model density.
Solution Approach 2:
The system dynamically determines which portions of the model to update based on capture device trajectory and observation data. By adaptively selecting active model portions that correspond to newly observed regions, the system optimizes processing time while maintaining dense mapping where needed.
2Measurement precision
If the system processes large amounts of continuous image data from moving capture devices, then the accuracy of the three-dimensional model is improved, but the computational complexity and processing load increase
Solution Approach 1:
The system extracts and processes only the relevant portions of image data that correspond to newly observed regions of the three-dimensional space. By filtering out redundant data from previously mapped areas, the system reduces computational complexity while maintaining model accuracy through selective processing of informative data.
Solution Approach 2:
The system applies different processing strategies to different regions of the model based on local requirements. Areas with high uncertainty or recent changes receive more intensive processing, while stable, well-mapped regions require minimal updates, optimizing the balance between accuracy and computational complexity.
3Adaptability or versatility
If the system handles unpredictable motion and loopy trajectories of the capture device, then the robustness of the mapping system is improved, but the difficulty of maintaining model consistency increases
Solution Approach 1:
The system uses feedback from the capture device trajectory and pose estimation to identify when the device returns to previously visited locations. This feedback mechanism enables loop closure detection and correction, maintaining model consistency by reconciling observations from different passes through the same space.
Solution Approach 2:
The system pre-establishes the relationship between model portions and capture device poses, creating a framework that anticipates potential loops and inconsistencies. By preparing registration and alignment structures in advance, the system can quickly resolve consistency issues when loopy trajectories occur without compromising overall model stability.
Data Source
AI summary
Certain examples described herein relate to modelling a three-dimensional space. Image data from at least one capture device is used to generate a three-dimensional model of the three-dimensional space. In certain examples, the three-dimensional model is segmented into active and inactive portions based on at least one model property. The examples are configured to use the active portions to update the three-dimensional model over time. Registration is also performed to align active portions of the three-dimensional model with inactive portions of the three-dimensional model over time. The registration aligns active portions of the three-dimensional model generated following an observation of a region of the three-dimensional space with inactive portions of the model generated following at least one previous observation of said region.