Unified Language, Image, And Point Cloud Representations for 3D Recognition
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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 aligning features from 3D point clouds with vision/language feature spaces using pre-trained models, enabling improved recognition through cross-modal training and fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D visual recognition models are trained using traditional methods with limited 3D datasets, then training cost and time are reduced, but recognition accuracy and generalization capability deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-training image and text encoders on large-scale 2D datasets (e.g., ImageNet, Wikipedia) before fine-tuning the 3D recognition model. This pre-training establishes robust feature representations that transfer to 3D tasks, improving recognition accuracy while reducing the time needed for 3D-specific training.
Solution Approach 2:
The patent introduces an intermediary approach by using 2D image encoders and text encoders as intermediate components to process 3D point cloud data. These intermediaries leverage pre-trained knowledge from 2D domains to enhance 3D recognition performance without requiring extensive 3D training data or time.
2Adaptability or versatility
If 3D visual recognition models use multi-modal cross-modal training frameworks, then generalization capability and recognition accuracy are improved, but system complexity and computational resources increase
Solution Approach 1:
The patent implements universality by designing a multi-modal framework where a single 3D recognition system performs multiple functions: 3D object classification, 3D retrieval, and zero-shot learning. The unified architecture processes different data types (point clouds, images, text) through shared encoders, achieving high generalization capability while managing system complexity through modular design.
Solution Approach 2:
The patent uses intermediary projection layers that map features from different modalities (3D point clouds, 2D images, text) into a unified feature space. These intermediaries enable cross-modal training and zero-shot learning by finding common representations, improving generalization without requiring complex task-specific architectures for each function.
3Measurement precision
If pre-trained vision-language models are used for generating image and text representations, then semantic alignment and recognition performance are enhanced, but computational cost and model size increase
Solution Approach 1:
The patent applies preliminary action by pre-training vision-language models on large-scale datasets before deploying them for 3D recognition tasks. The pre-trained models already contain aligned semantic representations of images and text, which are then fine-tuned for 3D applications. This approach enhances semantic alignment while reducing the computational cost of training from scratch.
Solution Approach 2:
The patent uses parameter changes by freezing certain parameters of the pre-trained vision-language models during fine-tuning while updating only the task-specific parameters. This selective parameter updating maintains semantic alignment capabilities while significantly reducing computational cost and energy consumption during the fine-tuning phase.
Data Source
AI summary
Systems and methods for training a neural network based three-dimensional (3D) encoder for 3D classification are provided. A training dataset including a plurality of samples is received, wherein a first sample includes an image, a text, and a point cloud. An image encoder of a pretrained vision and language model is used to generate image representations for the image of the first sample. A text encoder of the pretrained vision and language model is used to generate text representations for the text of the first sample. The neural network based 3D encoder is used to generate 3D representations for the point cloud of the first sample. A loss objective is computed based on the image representations, text representations, and 3D representations. Parameters of the neural network based 3D encoder are updated based on the computed loss objective via backpropagation.