3D Modeling Incident Scenes Using Time-of-Flight Depth Data
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Solution Overview
Problem
Incident scene investigations rely heavily on 2D images, which lack information about dimensions and object details, leading to inefficiencies in data analysis and investigation processes.
Innovation Solution
A 3D modeling system that uses time-of-flight cameras and metadata to generate and update 3D models of incident scenes, allowing for higher resolution capture of specific points of interest, enhancing data accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If 2D images are used for incident scene investigation, then the investigation process is simple and quick to capture, but the information about dimensions and object details is lost
Solution Approach 1:
The patent transforms 2D incident scene images into 3D models by integrating time-of-flight depth data with conventional image data. This dimensionality change preserves spatial information and object dimensions that are lost in traditional 2D photography, allowing investigators to view and measure the incident scene from multiple angles without losing critical dimensional data.
Solution Approach 2:
The system uses an intermediary processing system that combines data from multiple sensors (conventional cameras and time-of-flight sensors) to generate 3D models. This intermediary layer processes raw sensor data, aligns multiple data sources, and reconstructs the incident scene in three dimensions, bridging the gap between simple image capture and comprehensive dimensional analysis.
2Loss of information
If multiple images are captured to improve scene coverage, then the information completeness increases, but the time required for data analysis increases
Solution Approach 1:
The system segments the incident scene into multiple 3D point clouds from different camera positions and angles. Each segment is processed and aligned independently, then merged into a complete 3D model. This segmentation allows parallel processing of multiple images and reduces the overall analysis time compared to processing a single comprehensive image set.
Solution Approach 2:
The system creates multiple copies of the incident scene data from different perspectives and combines them into a unified 3D model. Rather than analyzing multiple separate 2D images, the system generates a single integrated 3D representation that contains all scene information, eliminating redundant analysis of the same spatial relationships across multiple images.
3Measurement precision
If high resolution images are captured for detailed analysis, then the measurement precision improves, but the data processing complexity increases
Solution Approach 1:
The system replaces manual measurement and analysis methods with automated 3D modeling and computer vision algorithms. High-resolution images and time-of-flight data are automatically processed to generate precise 3D measurements of objects and scene dimensions, eliminating the need for manual measurement and reducing processing complexity despite the high data volume.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides a more accurate and efficient method for incident scene investigation by creating detailed 3D models, reducing information loss and improving analysis efficiency.
Implementation Method 1
receiving, at the electronic processor, first metadata generated by a time-of-flight sensor corresponding to the one or more first images
Data Source
AI summary
Method and 3D modeling server (110) to generate a 3D model. The method includes receiving first images captured by a camera (320) corresponding to an incident scene and receiving first metadata generated by a time-of-flight sensor (325) corresponding to the first images. The method also includes generating a 3D model at a first resolution including a plurality of 3D points based on the first images and the first metadata and identifying a first incident-specific point of interest from the first images. The method further includes transmitting for recapturing the first incident-specific point of interest and receiving second images captured of the first incident-specific point of interest. The method also includes receiving second metadata generated corresponding to the second images and updating a first portion of the 3D model corresponding to the first incident-specific point of interest to a second resolution based on the second images and the second metadata.