Handheld RGB-D Scanner Real-Time 3D Model Creation
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Solution Overview
Problem
Conventional image capture systems for documenting structures or object shapes lack real-time capabilities and intensive image manipulation, failing to provide high-resolution, real-time 3D images with high frame rates, especially when capturing scenes with moving parts or from a moving device.
Innovation Solution
A handheld 3D image capturing system that combines an RGB-D camera with a Structured-Light or Time-of-Flight depth map generator, integrated with a tablet or smartphone, enabling real-time depth computation and high-resolution 3D model creation, allowing for precise location determination and on-the-fly image manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image capture systems are used, then device complexity is reduced, but real-time 3D imaging capability and measurement precision are lost
Solution Approach 1:
The patent combines multiple imaging sensors (RGB camera, depth sensor, structured light projector) into a single integrated handheld device. The image capture device includes an image sensor, depth sensor, and structured light projector that work together to capture 3D information, merging functions that would traditionally require separate systems into one unified platform.
Solution Approach 2:
The handheld device performs multiple functions: capturing 2D images, generating depth maps, creating 3D models, and providing measurement capabilities. The system can process various types of data (visual, depth, spatial) and output multiple formats (2D images, 3D models, point clouds), making it a universal imaging solution.
2Productivity
If real-time 3D imaging is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The system uses periodic structured light projection patterns to enable depth measurement. Instead of continuous operation, the structured light projector emits light in periodic patterns that reflect off the scene and return to the depth sensor, allowing real-time 3D imaging while reducing overall energy consumption through intermittent operation.
Solution Approach 2:
The system dynamically adjusts imaging parameters such as frame rate, resolution, and processing intensity based on scene complexity and user needs. The depth computation system can vary computational intensity and the image capture device can adjust capture frequency to balance productivity requirements with energy consumption.
3Measurement precision
If high-resolution 3D imaging is captured, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary depth map generation and 3D model creation during the image capture process itself. As the handheld device moves through the scene, the depth sensor continuously generates depth maps that are immediately processed into 3D models, eliminating post-processing time delays and enabling real-time high-resolution 3D imaging.
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 creation of ready-to-use 3D models in seconds with high precision and millimeter accuracy, streamlining workflows and improving user productivity by providing high-resolution, real-time 3D images and depth information, suitable for capturing scenes with moving parts or from a moving device.
Implementation Method 1
an image capture device for capturing and storing a plurality of digital images
Implementation Method 2
a depth computation system for deriving depth information for a plurality of arbitrary points in the images
Data Source
AI summary
A system and methods for real-time or near-real time processing and post-processing of RGB-D image data using a handheld portable device and using the results for a variety of applications. The disclosure is based on the combination of off-the-shelf equipment (e.g. an RGB-D camera and a smartphone/tablet computer) in a self-contained unit capable of performing complex spatial reasoning tasks using highly optimized computer vision algorithms. New applications are disclosed using the instantaneous results obtained and the wireless connectivity of the host device for remote collaboration. One method includes steps of projecting a dot pattern from a light source onto a plurality of points on a scene, measuring distances to the points, and digitally reconstructing an image or images of the scene, such as a 3D view of the scene. A plurality of images may also be stitched together to re-position an orientation of the view of the scene.