Real-time Image Undistortion for 3D Reconstruction
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Solution Overview
Problem
Existing 3D reconstruction systems face challenges in providing real-time, high-detail reconstructions without relying on cloud computing, especially in scenarios where GPS and IMU data are unavailable, and require significant computational resources and data transfer, limiting their applicability in time-sensitive operations like military reconnaissance and disaster relief.
Innovation Solution
A system utilizing a highly parallelized GPU-accelerated Structure from Motion (SfM) technique for real-time 3D reconstruction, which generates a level-of-detail textured model by undistorting images, calculating correction values, and partitioning them into grids for efficient rendering, allowing for local operation and reduced data transfer, and integrates feature matching and triangulation to refine camera poses and reconstruct 3D models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud computing is used for 3D reconstruction, then computational power and processing capability are improved, but data transfer requirements and system complexity increase
Solution Approach 1:
The patent extracts the computationally intensive 3D reconstruction processing from cloud-based systems and implements it locally on edge devices. By deploying SfM algorithms and GPU acceleration directly on portable hardware, the system eliminates the need for continuous cloud connectivity and large-scale data transfers while maintaining high computational performance for real-time reconstruction.
2Speed
If real-time 3D reconstruction is implemented, then processing speed and responsiveness are improved, but computational resource requirements and system complexity increase
Solution Approach 1:
The patent segments the 3D reconstruction pipeline into distinct modular components: image capture, feature detection, SfM processing, mesh generation, and rendering. Each module can be independently optimized and executed on appropriate hardware resources. The use of GPU acceleration specifically targets the computationally intensive SfM and mesh generation stages, enabling real-time performance without requiring complete system redesign.
Solution Approach 2:
The patent replaces traditional CPU-based processing with GPU-accelerated computation for the 3D reconstruction pipeline. By leveraging the parallel processing capabilities of GPUs, the system achieves real-time performance for computationally intensive tasks like bundle adjustment and mesh generation, significantly reducing processing time while maintaining manageable system complexity through hardware optimization.
3Measurement precision
If high-detail textured models are generated, then reconstruction accuracy and visual quality are improved, but computational load and processing time increase
Solution Approach 1:
The patent performs preliminary feature detection and keypoint extraction from images before executing the computationally intensive SfM reconstruction. By pre-processing images to identify and catalog features, the system reduces the complexity of subsequent reconstruction steps. The GPU acceleration then rapidly processes these pre-identified features through bundle adjustment and mesh generation, achieving high-detail results without excessive processing time.
Data Source
AI summary
A system provides image undistortion in 3D reconstruction. The system receives an image produced by a sensor, and determines whether correction values are cached for the sensor, where each correction value is configured to place a corresponding pixel into a corrected location. When there are no cached correction values, the system calculates correction values for pixels in the image, generates a correction grid for the image including vertices corresponding to texture coordinates from the image, where each vertex in the correction grid includes a corresponding correction value, partitions the correction grid into partitioned grids, and caches the partitioned grids. The system then renders the image using the partitioned grids.