Real-time 3D Volumetric Visualization Using GPU Compute Shaders
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Solution Overview
Problem
Traditional video systems using multi-spectral imaging for 3D volumetric data reconstruction require offline processing, which is time-consuming and does not allow for real-time visualization or verification of the reconstruction results during the capture process.
Innovation Solution
Implementing a method that captures video data using multiple cameras, calculates depth information, generates a 3D point cloud, and renders a visualization image in real-time using compute shaders, allowing for immediate presentation in a 3D environment, with the option to apply color information and account for lens distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If offline reconstruction process is used, then manufacturing precision of 3D volumetric data is improved, but loss of time increases significantly
Solution Approach 1:
The patent replaces traditional CPU-based offline reconstruction with GPU-based parallel processing using compute shaders. This substitution of computational architecture enables real-time volumetric reconstruction by leveraging the GPU's massive parallel processing capability to perform depth calculation, point cloud generation, and visualization simultaneously during data capture, resolving the contradiction between reconstruction quality and processing time.
Solution Approach 2:
The system performs preliminary actions by pre-configuring the GPU compute shaders with all necessary reconstruction algorithms and parameters before data capture begins. The depth information calculation, 3D point cloud generation, and visualization pipelines are pre-established, allowing immediate real-time processing during capture without requiring post-processing reconstruction steps.
2Productivity
If real-time visualization is implemented, then productivity of data verification is improved, but device complexity increases
Solution Approach 1:
The GPU compute shader system performs multiple functions within a single unified architecture: it calculates depth information from 2D images, generates 3D point clouds, applies color mapping, and renders real-time visualizations. This multi-functional approach enables real-time verification while avoiding the need for separate dedicated hardware components for each processing stage, thus improving productivity without proportionally increasing device complexity.
3Measurement precision
If multiple cameras are used for multi-spectral imaging, then measurement precision of 3D data is improved, but device complexity increases
Solution Approach 1:
The patent uses GPU-based parallel computation to handle the complex data fusion and depth calculation from multiple cameras. Instead of requiring complex mechanical synchronization and processing hardware, the system leverages the GPU's parallel processing architecture to simultaneously process images from multiple cameras, calculate depth information, and generate 3D reconstructions in real-time, maintaining measurement precision while managing device complexity through computational efficiency.
Data Source
AI summary
Capturing and visualizing video, including: capturing video data using a plurality of cameras; sending the captured video data to a first shader; calculating depth information at the first shader using the captured video data; generating a three-dimensional (3-D) point cloud using the depth information; and rendering a visualization image using the 3-D point cloud.