Adaptive Offset Convolution for 3D Reconstruction Accuracy
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Solution Overview
Problem
Conventional 3D reconstruction using monocular speckle structured light systems is limited by speed and accuracy, primarily due to inefficient back-end calculation methods and preprocessing of single-channel images, which restricts its applications to low-accuracy scenarios.
Innovation Solution
Implementing an adaptive offset term using convolution techniques in local adaptive binarization expressions, processed by a convolutional neural network on an edge device, to enhance the accuracy and speed of 3D reconstruction by separating speckle patterns from background images and eliminating error points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional local adaptive binarization methods are used, then the preprocessing is simpler, but the matching accuracy and speed of back-end calculations are limited
Solution Approach 1:
The patent modifies the binarization threshold by adding an adaptive offset term that varies across different regions of the image. This offset term is calculated based on local statistics (mean and standard deviation) and adjusts the threshold dynamically to accommodate varying lighting conditions and speckle patterns, thereby improving matching accuracy without requiring completely new preprocessing architecture
Solution Approach 2:
The patent divides the image into multiple local regions and performs binarization independently for each region with its own adaptive offset. This segmentation allows the preprocessing to adapt to local characteristics of speckle patterns and lighting variations, improving overall matching accuracy while maintaining computational efficiency through parallel processing of regions
2Productivity
If conventional binarization methods are used, then the computational load is lower, but the 3D reconstruction speed is not fast enough
Solution Approach 1:
The patent performs adaptive offset calculation and binarization as preliminary processing steps before the main 3D reconstruction algorithm. By pre-processing the image data with optimized binarization that accounts for local variations, the subsequent matching and reconstruction stages operate on already-optimized data, significantly improving overall reconstruction speed without adding computational burden during the critical reconstruction phase
3Measurement precision
If conventional binarization methods are used, then the processing is faster, but the accuracy of 3D reconstruction is not accurate enough
Solution Approach 1:
The patent applies different binarization thresholds to different regions of the image based on local characteristics. Each region receives a customized offset term that adapts to its specific lighting conditions and speckle pattern properties. This local quality approach ensures high accuracy in regions with challenging conditions while maintaining reasonable processing speed through efficient localized computations
Data Source
AI summary
An apparatus comprising an interface, a structured light projector and a processor. The interface may receive pixel data. The structured light projector may generate a structured light pattern. The processor may process the pixel data arranged as video frames, perform operations using a convolutional neural network to determine a binarization result and an offset value and generate disparity and depth maps in response to the video frames, the structured light pattern, the binarization result, the offset value and a removal of error points. The convolutional neural network may perform a partial block summation to generate a convolution result, compare the convolution result to a speckle value to determine the offset value, generate an adaptive result in response to performing a convolution operation, compare the video frames to the adaptive result to generate the binarization result for the video frames, and remove the error points from the binarization result.