3D Image Reconstruction Using Plane Detection and Edge-Preserving Smoothing
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Solution Overview
Problem
Current methods for reconstructing three-dimensional images using multiple cameras struggle to effectively detect planes and smooth depth information, leading to inconsistencies and reduced realism in the generated 3D images.
Innovation Solution
A method and apparatus that generate an initial 3D image by matching images from multiple cameras, segmenting the images, detecting planes, and filtering depth information to create a high-density 3D image, utilizing techniques such as scale-invariant feature transform (SIFT) for characteristic point extraction, epipolar geometry for matching, and random sample consensus (RANSAC) for plane detection, while applying edge-preserving smoothing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If image matching is performed using multiple cameras to generate initial 3D image, then depth information is obtained, but plane detection accuracy deteriorates due to noise and inconsistencies in the initial 3D data
Solution Approach 1:
The patent segments the initial 3D image into multiple local regions and performs plane detection independently in each region. This segmentation allows the algorithm to focus on local planar structures without being overwhelmed by global noise, thereby improving plane detection accuracy while preserving depth information from the multi-camera system.
Solution Approach 2:
The patent performs preliminary processing steps including noise filtering and depth map refinement before plane detection. By preprocessing the initial 3D data to remove obvious noise and inconsistencies, the subsequent plane detection algorithm operates on cleaner data, improving accuracy without losing essential depth information.
2Reliability
If smoothing is applied to depth information to improve realism, then visual quality is enhanced, but depth discontinuities and edges may be lost
Solution Approach 1:
The patent applies different smoothing intensities to different regions of the depth map. In regions identified as planar, stronger smoothing is applied to enhance realism, while in regions containing edges or depth discontinuities, smoothing is reduced or avoided to preserve structural accuracy. This local differentiation resolves the contradiction between realism and precision.
Solution Approach 2:
The patent uses the detected plane information as feedback to guide the smoothing process. After plane detection, the algorithm identifies reliable planar regions and uses this information to control where smoothing should be applied, ensuring that smoothing enhances realism in appropriate areas while preserving edges where depth discontinuities indicate important structural boundaries.
3Manufacturing precision
If characteristic point extraction and matching is performed to generate 3D coordinates, then 3D image is reconstructed, but processing time increases due to computational complexity
Solution Approach 1:
The patent divides the image processing task into multiple stages: characteristic point extraction, initial matching, then region-based refinement. By segmenting the processing pipeline and focusing computational resources on critical steps, the system maintains high reconstruction accuracy while reducing overall processing time through efficient resource allocation.
Solution Approach 2:
The patent performs preliminary characteristic point extraction and coarse matching to establish initial 3D correspondences. This preliminary action provides a rough but accurate framework that reduces the computational burden of subsequent refinement steps, as the algorithm only needs to refine rather than compute from scratch, thereby reducing processing time while maintaining accuracy.
Data Source
AI summary
Apparatus and method for reconstructing a high-density three-dimensional (3D) image are provided. The method includes: generating an initial 3D image by matching a first image captured using a first camera and a second image captured using a second camera; searching for a first area and a second area from the initial 3D image by using a number of characteristic points included in the initial 3D image; detecting a plane from a divided first area; filtering a divided second area; and synthesizing the detected plane and the filtered second area.