View Angle Conversion Network for 3D Feature Identification
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Solution Overview
Problem
Traditional image synthesis methods for a single view are affected by observation angles, leading to loss of spatial information and reduced quality and efficiency in synthesized three-dimensional images, with ineffective identification of three-dimensional features.
Innovation Solution
A method and device utilizing a pre-designed generative adversarial network model to convert images of a target object into different view angles, trained with model training data including planar images and labels, allowing for three-dimensional reconstruction and feature identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional image synthesis methods for a single view are used, then the process is simple, but spatial information is lost and image quality deteriorates
Solution Approach 1:
The patent transforms 2D planar images into 3D spatial representations by generating images at multiple view angles. The view angle conversion network synthesizes virtual views from a single input image, effectively adding the dimensional aspect of viewing angle to recover spatial information that would otherwise be lost in single-view synthesis.
Solution Approach 2:
The patent creates multiple copies of the object from different virtual viewpoints by generating synthesized images at various view angles. Instead of capturing multiple physical views, the system generates synthetic copies of the object appearance from angles not present in the original input, thereby reconstructing spatial relationships through virtual replication.
2Measurement precision
If traditional single view synthesis is used, then the method is simple, but three-dimensional feature identification becomes ineffective
Solution Approach 1:
The patent performs preliminary view angle conversion before three-dimensional feature analysis. By pre-generating multiple virtual views and constructing a view angle feature matrix, the system prepares comprehensive spatial information in advance, making subsequent 3D feature extraction more accurate and efficient rather than attempting to infer depth from a single view.
Solution Approach 2:
The patent transforms the representation parameters by converting images from a single fixed viewpoint to multiple dynamic viewpoints. The view angle conversion network modifies the angular parameters of image capture, generating a series of images with varying viewing angles that encode three-dimensional structural information in a way that facilitates accurate 3D feature identification.
3Productivity
If images are synthesized from a single observation angle, then the process is straightforward, but image synthesis quality and efficiency are reduced
Solution Approach 1:
The patent applies periodic action through the generative adversarial network's iterative training process. The GAN structure involves repeated cycles of generator creation and discriminator evaluation, where the view angle conversion network is trained through multiple epochs to progressively improve synthesis quality, achieving both high efficiency and quality through this periodic optimization process.
Data Source
AI summary
An image view angle conversion method includes: model training data are obtained, the model training data including planar images of a training object at a plurality of different view angles and labels corresponding to respective view angles, where the labels corresponding to the different view angles are different. A pre-designed generative adversarial network model is trained according to the model training data to obtain a view angle conversion network model. A planar image of a target object and labels corresponding to one or more expected view angles of the target object are input into the view angle conversion network model, so that the view angle conversion network model generates planar images of the target object at the expected view angles.