3D Dataset Generation for Neural Network Training
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Solution Overview
Problem
Conventional 3D modeling techniques require expensive 3D imaging sensors and substantial manual effort for 3D reconstruction, especially when creating multiple 3D models, and involve complex texture mapping processes.
Innovation Solution
An electronic device and method for generating a 3D dataset from 2D images to train a neural network for 3D reconstruction, which detects 2D landmarks, aligns 3D shape models, and estimates texture mapping information to produce textured or untextured 3D models with minimal manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional 3D modeling techniques using RGB-depth cameras or 3D scanning setups are used, then depth information and color/texture information can be acquired, but the process becomes inefficient and costly when large numbers of 3D models are required
Solution Approach 1:
The patent creates synthetic 3D models by copying and transforming 2D images through geometric transformations and depth map generation, rather than requiring physical 3D scanning. This allows unlimited replication of 3D models from simple 2D inputs without additional scanning hardware or manual effort
Solution Approach 2:
The patent replaces mechanical 3D scanning systems with computational image processing methods. Instead of using physical depth sensors and scanning mechanisms, the system uses 2D images combined with neural networks and geometric algorithms to generate 3D models, eliminating complex scanning setups
2Quantity of substance
If multiple 3D models are required for various applications, then the demand for 3D content increases, but the manual effort and time required for creation increases proportionally
Solution Approach 1:
The system generates unlimited 3D model variations by copying the core 3D structure and applying different transformations, textures, and parameters. A single 2D image can produce multiple 3D models with different viewpoints, scales, and stylistic variations without additional manual work
Solution Approach 2:
The patent generates diverse 3D models by changing parameters such as depth map values, transformation matrices, and neural network weights. By modifying these parameters, the system can create numerous distinct 3D models from the same 2D input, dramatically increasing output quantity without proportional time investment
3Manufacturing precision
If conventional texture mapping processes are used for 3D reconstruction, then realistic surface appearance can be achieved, but the process becomes complex and time-consuming
Solution Approach 1:
The patent replaces complex manual texture mapping processes with automated neural network-based texture synthesis. The system uses 2D images as direct texture sources and applies them to 3D models through parameterized transformations, eliminating the need for manual UV mapping and texture authoring while maintaining visual fidelity
Solution Approach 2:
The system performs self-service texture mapping by automatically generating and applying textures to 3D models without human intervention. The neural networks and algorithms autonomously handle texture synthesis, coordinate mapping, and surface projection, reducing the process from a complex manual task to an automated pipeline
Data Source
AI summary
An electronic device receives a set of 2D images comprising at least first 2D image of an object of interest and detects a plurality of 2D landmarks on the first 2D image. The detected plurality of 2D landmarks corresponds to shape-features of the object of interest. The electronic device aligns a first three-dimensional (3D) shape model of a reference object by fitting 2D landmarks of the first 3D shape model to the detected plurality of 2D landmarks. The electronic device estimates texture mapping information between the object of interest and the aligned first 3D shape model and generates a dataset by including the first 2D image, the aligned first 3D shape model, and the estimated texture mapping information as a first sample of the dataset. Based on the generated dataset, the electronic device trains a neural network model on a task of 3D reconstruction from a single 2D image.