Multimodal 3D Asset Search via Unified Embedding Space
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Solution Overview
Problem
Existing digital asset retrieval systems struggle to efficiently search for three-dimensional object representations using inputs in multiple modes, such as text, two-dimensional images, or three-dimensional models, due to limitations in generalizing from 3D data and requiring extensive manual annotation.
Innovation Solution
The system employs a pre-trained text/image co-embedding architecture that generates a rich search space by rendering multiple views of 3D models and encoding them using machine learning models like visual encoders, allowing for the identification of 3D object representations based on inputs in various modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing digital asset retrieval systems are used to search for three-dimensional object representations, then the search can be performed using conventional tools, but the system cannot efficiently handle multiple input modes (text, 2D images, 3D models) and requires extensive manual annotation
Solution Approach 1:
The patent applies universality by creating a unified search space that accepts multiple input modes (text queries, 2D images, and 3D models) through a single system architecture. The system uses different encoding models for different input types but maps them all to the same 3D object representation space, enabling one system to perform multiple retrieval functions without requiring separate specialized tools for each input mode.
Solution Approach 2:
The patent uses encoding models as intermediary components that translate different input modes into a common representation space. Specifically, text is encoded using language models, 2D images using visual encoders, and 3D models using view-rendering and encoding pipelines, all of which produce embeddings that can be searched against the same 3D object database, thereby mediating between diverse inputs and the unified search space.
2Measurement precision
If conventional search tools are used for three-dimensional content, then the implementation is simpler, but the search capability and accuracy are insufficient
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing encoded representations of 3D objects in a structured search space before actual queries are made. The system pre-processes 3D models by rendering multiple views and encoding them into the search space, so that when a query arrives, the system can quickly compare it against pre-computed representations rather than processing everything from scratch, thereby improving search accuracy while managing implementation complexity.
Solution Approach 2:
The patent transitions from conventional 2D image search to 3D object representation search by introducing dimensional transformation. The system renders 3D models from multiple viewpoints and encodes them into a high-dimensional search space that captures geometric and semantic information, allowing queries to match 3D objects based on spatial relationships and structural properties that cannot be captured in traditional 2D search dimensions.
3Reliability
If manual annotation is used extensively to train the system, then the model accuracy improves, but the time and resource consumption increases significantly
Solution Approach 1:
The patent uses copying by leveraging pre-trained encoding models from existing language and vision models rather than training new models from scratch with extensive manual annotations. The system copies and adapts proven encoding architectures (language models for text, visual encoders for images) and fine-tunes them for 3D retrieval tasks, significantly reducing the need for manual annotation while maintaining model accuracy through transfer learning.
Data Source
AI summary
A computing system receives a query for a three-dimensional representation of a target object. The query comprises input in the form of text describing the target object, a two-dimensional image of the target object, or a three-dimensional model of the target object. The computing system encodes the input using a machine learning model to generate an encoded representation of the input. The computing system searches a search space using nearest neighbors to identify a three-dimensional representation of the target object. The search space comprises encoded representations of multiple views of a plurality of sample three-dimensional object representations. The computing system outputs the identified three-dimensional representation of the target object.