Pixel Embedding Material Selection for Accurate Image Masking
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Solution Overview
Problem
Existing software tools struggle to accurately select pixels corresponding to a specific material in images due to the complexity of material appearance, which is influenced by various factors such as geometry, lighting, and the sheer number of materials, and they fail to automatically select multiple objects with the same material across an image.
Innovation Solution
A material selection model is trained using a contrastive loss to generate pixel embeddings, identifying pixels corresponding to a target material by generating embeddings for anchor and other pixels, and updating the model based on a contrastive loss, utilizing procedurally generated training images to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing software tools use traditional object selection methods (boundary drawing), then users can select objects in images, but the tools fail to accurately select pixels corresponding to specific materials due to material appearance complexity
Solution Approach 1:
The patent applies preliminary action by pre-training a material selection model using procedurally generated training images before actual material selection tasks. The model is trained offline with synthetic data containing various materials under different lighting and geometric conditions, so that when the model is deployed, it already possesses the knowledge to accurately identify material pixels without requiring complex real-time processing or manual intervention.
Solution Approach 2:
The patent introduces an intermediary element - a trained machine learning model that acts as a mediator between the input image and the material selection output. This model translates complex visual information about material appearance into accurate pixel-level material identification, resolving the contradiction by delegating the complex recognition task to a specialized intermediary system rather than using simple boundary-drawing tools.
2Productivity
If the model is trained to identify materials explicitly, then material classification can be achieved, but the selection process becomes slower and less efficient
Solution Approach 1:
The patent applies the taking out principle by extracting the material identification task from the overall material selection process. Instead of requiring the model to explicitly identify and classify materials (which would be slow), the model is trained to directly output pixel-level material masks. This extraction separates the identification function from the classification function, enabling faster processing while maintaining accuracy by focusing the model's output directly on the selection task.
3Adaptability or versatility
If traditional selection tools are used, then simple object boundaries can be selected, but multiple objects with the same material across the image cannot be automatically selected
Solution Approach 1:
The patent applies universality by designing a material selection model that can handle multiple types of selection tasks through a single unified system. The model is trained on diverse training images containing various materials and objects, enabling it to automatically select any material type (wood, metal, plastic, etc.) across multiple objects in a single operation. This multi-functional capability eliminates the need for separate tools for different material types while maintaining ease of operation through consistent interaction.
Data Source
AI summary
A model is trained to predict pixels from an image that correspond to a material of a selected pixel using contrastive loss. A training dataset comprising a training image and material information for the training image is received. An anchor pixel in the training image is identified. To train the model using the training dataset, the model generates embeddings for pixels of the training image, including the anchor pixel and a plurality of other pixels. A contrastive loss is determined based on a comparison of the embeddings to the material information. The model is updated based on the loss.