3D Synthetic Image Generation for Novel Viewpoint Annotation
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Solution Overview
Problem
Accurate automated damage assessment models require large amounts of training data, which is limited by conventional techniques that rely solely on camera-captured images, and these methods struggle to generate annotations for novel viewpoints.
Innovation Solution
Generate synthetic images from novel viewpoints using three-dimensional representations of objects and backgrounds, apply texturization techniques, and utilize Generative Adversarial Networks (GANs) to enhance realism and automatically propagate annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If synthetic images are generated using traditional rendering methods, then the training dataset size is enlarged, but the images lack realism and reduce model accuracy
Solution Approach 1:
The patent creates synthetic copies of real images through 3D reconstruction and rendering. A 3D mesh model is constructed from multi-view captured images, then used to generate synthetic images from novel viewpoints. These synthetic copies augment the training dataset while maintaining visual consistency with real objects, resolving the contradiction between dataset size and image realism.
Solution Approach 2:
The patent introduces a GAN-based translation model as an intermediary between rendered synthetic images and realistic target images. The GAN learns the mapping from rendered images to photorealistic images, acting as a mediator that transforms synthetic images into realistic ones, thereby improving model accuracy while maintaining dataset expansion benefits.
2Adaptability or versatility
If more training data is collected using additional camera positions, then the viewpoint coverage is improved, but the complexity and cost of data collection increases
Solution Approach 1:
The patent transitions from 2D image capture to 3D modeling by constructing a 3D mesh representation of the object. This 3D model enables generation of images from any viewpoint without requiring physical cameras at those positions, thus expanding viewpoint coverage while avoiding the complexity of multi-camera systems.
Solution Approach 2:
Instead of physically capturing images from multiple viewpoints using additional cameras, the patent creates virtual copies of the object from a 3D model. These synthetic images simulate views from novel camera positions, achieving comprehensive viewpoint coverage without the hardware complexity of deploying multiple physical cameras.
3Measurement precision
If manual annotation is performed for each training image, then the annotation accuracy is improved, but the time consumption and resource usage increase significantly
Solution Approach 1:
The patent copies annotations from the 3D mesh model to synthetic images. Since the 3D model contains geometric and semantic information about the object, annotations can be automatically projected onto rendered views, maintaining accuracy while eliminating manual annotation time for synthetic images.
Solution Approach 2:
The system enables self-annotation by automatically projecting 3D model data and existing annotations onto synthetic images. The 3D mesh structure inherently contains spatial relationships and object boundaries that can be directly translated into image annotations without human intervention, achieving both accuracy and efficiency.
Data Source
AI summary
A plurality of images of an object and a background may be captured. Three-dimensional representations of the background and the object may be generated based on the captured images. A synthetic image of the object and the background may be rendered. The synthetic image may depict a two-dimensional view of the object having a novel viewpoint different from the viewpoints of the captured images. A corrected synthetic image may be generated. The corrected synthetic image may be stored on a storage medium.