3D Vertex Descriptor Training for Explainable Topology Recognition
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Solution Overview
Problem
Existing methods for training machine learning models to recognize object topology from images, such as self-supervised dense descriptor learning, rely heavily on collected training data and lack explainability, making them inefficient and inflexible.
Innovation Solution
A method involving obtaining a 3D model of an object, determining descriptors for its vertices by minimizing weighted distances, generating training data image pairs with target images, and training a machine learning model using supervised learning to recognize object topology from images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-supervised dense descriptor learning is used to train the machine learning model, then the model can recognize object topology from images, but the training data requirements are excessive and the descriptors lack explainability
Solution Approach 1:
The patent introduces 3D model vertices as intermediary elements that bridge the gap between 2D images and object topology. By projecting 3D vertex descriptors onto 2D image space, the system creates a structured intermediate representation that guides the learning process, reducing reliance on large amounts of training data while maintaining recognition accuracy
Solution Approach 2:
The patent performs preliminary computation of vertex descriptors and target image generation before the actual training process. By pre-computing the ground truth descriptors from 3D models and preparing target images in advance, the system eliminates the need for extensive training data collection, as the supervised learning process starts with already-structured target information
2Adaptability or versatility
If self-supervised learning is used for descriptor determination, then the model can operate with limited labeled data, but the descriptors lack explainability and the approach is inflexible
Solution Approach 1:
The 3D vertex descriptors serve as an explainable intermediary that provides geometric interpretation to the learned features. Each vertex descriptor can be traced back to a specific 3D location and its relationships to other vertices, providing inherent explainability about what the model is learning while maintaining flexibility in handling various object poses and viewpoints
Solution Approach 2:
The patent changes the parameter space from raw pixel values to structured vertex descriptor spaces. By representing objects in terms of geometric parameters (vertex positions, edge weights, spatial relationships) rather than pixel intensities, the system achieves both explainability (through geometric interpretation) and flexibility (through pose-invariant representations)
3Measurement precision
If extensive training data is collected for supervised learning, then the model achieves better accuracy, but the training process becomes more time-consuming and resource-intensive
Solution Approach 1:
The patent performs preliminary generation of target images and vertex descriptors from 3D models before training begins. This pre-computation creates a compact, structured training dataset that captures the essential geometric relationships, allowing the model to learn efficiently from a small number of carefully constructed examples rather than requiring extensive data collection and processing time
Data Source
AI summary
A method for training a machine learning model for recognizing an object topology of an object from an image of the object. The method includes obtaining a 3D model of the object, wherein the 3D model comprises a mesh of vertices connected by edges, wherein each edge has a weight which specifies proximity of two vertices connected by the edge in the object; determining a descriptor for each vertex of the mesh by searching descriptors for the vertices which minimize the sum, over pairs of connected vertices, of distances between the descriptors of the pair of vertices weighted by the weight of the edge between the pair of vertices; generating training data image pairs, wherein each training data image pair comprises a training input image showing the object and a target image; and training the machine learning model by supervised learning using the training data image pairs as training data.