Multi-device 3D Depth Capture via Segmentation and Feedback
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Solution Overview
Problem
Current 3D data capture technologies using single cameras result in data gaps and require extensive post-processing to create immersive environments, which is time-consuming and requires technical expertise, while they fail to capture dynamic scenes effectively due to limitations in depth information and motion representation.
Innovation Solution
A system employing multiple portable data capture devices capable of capturing 3D depth video data from various perspectives simultaneously, using techniques like LiDAR, structured light, and multiple view stereo, which can communicate and process data in real-time to minimize gaps and enable immersive, dynamic 3D environments without the need for extensive post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single camera is used to capture 3D data, then device complexity is reduced, but data gaps increase and measurement precision deteriorates
Solution Approach 1:
The system divides the data capture task into multiple segments by using multiple portable data capture devices positioned at different locations. Each device captures depth data from its specific viewpoint, and the system segments the overall environment into multiple viewable areas, reducing data gaps that would exist with a single camera.
Solution Approach 2:
The system transitions from a single-point perspective to a multi-point spatial distribution of cameras. By positioning cameras at different locations in three-dimensional space around the environment, the system captures depth information from multiple angles simultaneously, eliminating the need to reposition a single camera and reducing data gaps.
2Measurement precision
If multiple portable data capture devices are used to reduce data gaps, then measurement precision improves, but device complexity and processing time increase
Solution Approach 1:
Each portable data capture device is designed as a universal unit capable of performing all necessary functions: capturing depth data, capturing color images, tracking its own location and orientation, and communicating with other devices. This multi-functionality reduces system configuration complexity because each device is self-sufficient and interchangeable.
Solution Approach 2:
Each data capture device independently tracks its own location and orientation using onboard sensors (accelerometers, gyroscopes, GPS), and autonomously captures and transmits its data. This self-service capability eliminates the need for complex external synchronization systems and reduces overall system complexity.
3Productivity
If multiple cameras capture data simultaneously from various perspectives, then productivity improves, but loss of information increases due to synchronization challenges
Solution Approach 1:
The system implements continuous feedback loops where each camera records its location and orientation data alongside its depth and image data. This feedback mechanism ensures that temporal and spatial information is accurately tagged with each data capture event, maintaining synchronization without requiring complex inter-camera communication protocols.
Solution Approach 2:
Each portable data capture device pre-configures its onboard sensors (accelerometers, gyroscopes, GPS) to continuously track its position and orientation before actual data capture begins. This preliminary action ensures that when multiple cameras capture data simultaneously, all spatial-temporal reference information is already available, eliminating synchronization delays and information loss.
4Measurement precision
If extensive post-processing is performed to create immersive environments, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system performs preliminary data processing and spatial registration during the data capture phase itself. Each device tags its data with precise location and orientation information, and the system begins integrating data from multiple devices in real-time, reducing the amount of post-processing required and minimizing time loss.
Solution Approach 2:
The system replaces complex mechanical post-processing operations with computational algorithms that automatically register and integrate data from multiple cameras based on their GPS coordinates, accelerometer, and gyroscope data. This substitution of computational methods for manual or mechanical processing significantly reduces processing time while maintaining high measurement precision.
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
Enables the capture of true 3D depth video data with minimal technical knowledge, allowing for immersive and dynamic 3D digital environments that can be experienced in real-time or through stored data, reducing data gaps and processing time while providing accurate and efficient data representation of changing scenes.
Implementation Method 1
A LiDAR system will then calculate the distance from the light source to the surface based on the round-trip time of the light (known as 'time of flight' (TOF) data)
Implementation Method 2
LiDAR directs light from a light source at a surface that reflects the light back to a receiver
Data Source
AI summary
Embodiments of the invention describe apparatuses, systems, and methods related to data capture of objects and/or an environment. In one embodiment, a user can capture time-indexed three-dimensional (3D) depth data using one or more portable data capture devices that can capture time indexed color images of a scene with depth information and location and orientation data. In addition, the data capture devices may be configured to captured a spherical view of the environment around the data capture device.