3D Model Part Retrieval via Shape Embeddings
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Solution Overview
Problem
The existing methods for generating 3D models from 2D images are time-consuming and require significant expertise, making the process costly and inefficient, especially for complex objects, as they rely heavily on manual artist intervention.
Innovation Solution
The use of machine learning techniques, specifically multi-headed convolutional neural networks, to automatically identify and retrieve 3D model parts from a library, modify them to match the input 2D image data, and combine these parts to generate a complete 3D model, along with predicting Physically-Based Rendering (PBR) materials for enhanced photorealism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual artist intervention is used to generate 3D models from 2D images, then the quality and realism of the 3D models are improved, but the time required and cost increase significantly
Solution Approach 1:
The system segments the 3D modeling process into distinct components: 2D image input, automated part identification, 3D model retrieval, and assembly. By breaking down the complex task of creating realistic 3D models into manageable segments, the system achieves both high quality output and reduced time requirements, eliminating the need for manual artist intervention while maintaining manufacturing precision.
2Manufacturing precision
If manual artist intervention is used to generate 3D models from 2D images, then the quality and realism of the 3D models are improved, but the cost increases significantly
Solution Approach 1:
The system enables self-service automated 3D modeling by using machine learning algorithms to automatically identify object parts in 2D images, retrieve corresponding 3D models, and assemble them. This eliminates the need for expensive manual artist intervention while maintaining high-quality output, directly reducing manufacturing costs without sacrificing 3D model quality.
3Productivity
If automated methods are used to generate 3D models from 2D images, then the time required and cost are reduced, but the quality and realism of the 3D models decrease
Solution Approach 1:
The system replaces manual mechanical processes with automated machine learning-based processes. Convolutional neural networks automatically analyze 2D images to identify object parts, and automated retrieval systems fetch corresponding 3D models. This substitution maintains high productivity while ensuring manufacturing precision through sophisticated algorithms that capture complex visual features and relationships.
Solution Approach 2:
The system transforms the modeling process by changing key parameters: using deep learning feature extraction instead of manual modeling, automated part segmentation instead of manual segmentation, and intelligent 3D model retrieval based on learned features. These parameter changes enable the system to achieve both high productivity and manufacturing precision simultaneously by optimizing the automated workflow.
Data Source
AI summary
Devices and techniques are generally described for retrieving three dimensional part models. Two-dimensional (2D) image data representing an object with at least a first part and a second part may be received. A first machine learning model may be used to generate a first shape embedding representing the first part and a second shape embedding representing the second part. A first 3D model stored in a non-transitory computer-readable memory that represents the first part may be determined based at least in part on the first shape embedding. A second 3D model stored in the non-transitory computer-readable memory that represents the second part may be determined based at least in part on the second shape embedding.