Unified 3D Recognition From Image, Language, And Point Cloud Triplets
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Solution Overview
Problem
3D visual recognition models are limited by small datasets and high data collection and annotation costs, hindering their generalization and real-world applications.
Innovation Solution
A 3D visual recognition framework that learns unified representations of language, image, and point cloud by pre-training a 3D backbone model on object triplets, aligning features across modalities to leverage abundant semantics and improve recognition ability, with methods to create triplets from existing datasets without manual annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D visual recognition models are trained using traditional methods with limited datasets, then the model structure remains simple, but the recognition performance and generalization ability are insufficient
Solution Approach 1:
The patent combines multiple data modalities (2D images, text descriptions, and 3D point clouds) into unified triplet samples for training. This merging of different data types allows the model to learn richer representations without requiring exponentially larger 3D datasets, directly addressing the limitation of small 3D data quantities while improving recognition performance.
Solution Approach 2:
The patent pre-trains the 3D backbone model using synthesized triplet data generated from existing 3D models before fine-tuning on real data. This preliminary action creates a robust initialization that improves generalization performance, allowing the model to achieve better recognition accuracy even with limited real 3D training samples.
2Measurement precision
If manual annotation is used to create training datasets, then data quality is high, but the data collection and annotation costs are high
Solution Approach 1:
The patent synthesizes training triplet data by copying and transforming existing 3D models to generate corresponding 2D images and point clouds with automatic text descriptions. This copying approach creates high-quality training data without manual annotation, dramatically reducing data collection costs while maintaining sufficient data quality for effective model training.
Solution Approach 2:
The system automatically generates text descriptions and corresponding 2D images from 3D models without human intervention. This self-service data generation process eliminates the need for expensive manual annotation while producing consistent, high-quality training triplets that maintain the semantic relationships needed for effective learning.
3Adaptability or versatility
If 3D datasets are expanded to improve model generalization, then recognition performance improves, but the data collection cost increases
Solution Approach 1:
The patent creates a universal training framework using triplet samples that can be generated from any 3D model across different categories and domains. This multi-functional approach allows the same training methodology to improve generalization across various 3D recognition tasks without requiring domain-specific data collection, maintaining cost efficiency while enhancing adaptability.
Data Source
AI summary
A method of training a neural network based three-dimensional (3D) encoder is provided. A training dataset is generated using a plurality of 3D models of a 3D model dataset. To generate a first sample of the training dataset, an image generator with multi-view rendering is used to generate a plurality of image candidates of a first 3D model. A word is chosen from metadata associated with the first 3D model. A language model is used to generate one or more text descriptions using the selected word and a plurality of prompts. A point cloud is generated by randomly sampling points in the 3D model. The first sample is generated to include a first image randomly selected from the plurality of image candidates, one or more text descriptions, and the point cloud is generated. The 3D encoder is trained using the training dataset including the first sample.