3D Image Feature Training Across Multiple View Angles
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Solution Overview
Problem
Existing image processing models fail to effectively leverage the relationship between images at different angles of view, limiting the extraction of feature information and geometric perception capabilities.
Innovation Solution
A model training method that increases similarity between feature information of the same point in images from different angles of view using a first loss term, and reduces similarity between different points, enhancing geometric perception and information integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If feature extraction is performed separately on images at different angles of view without learning relationships between them, then the processing simplicity is maintained, but the feature information completeness and geometric perception capability are insufficient
Solution Approach 1:
The patent merges the feature extraction process across multiple images by introducing a loss function that explicitly models relationships between corresponding points in different angle images. This combines previously separate extraction operations into a unified process that leverages cross-image geometric constraints, thereby improving feature information completeness without requiring a completely new architectural paradigm.
Solution Approach 2:
The patent implements feedback through the loss function that measures the discrepancy between extracted features and their corresponding features in other angle images. This feedback mechanism guides the feature extraction process to produce more consistent and informative features by continuously adjusting based on inter-image relationships, effectively addressing information loss without excessive complexity.
2Reliability
If a model integrates information from multiple angles of view, then geometric perception capability is enhanced, but the computational resources and processing time increase
Solution Approach 1:
The patent changes the parameter space by introducing a loss function that operates on feature similarity metrics between angles. This allows the model to enhance geometric perception by adjusting parameter relationships rather than simply accumulating more data, making the computational process more efficient while improving reliability.
Solution Approach 2:
The loss function serves multiple purposes simultaneously: it enforces feature consistency across angles, learns geometric relationships between images, and guides the feature extraction process. This multi-functionality reduces the need for separate processing stages, thereby enhancing geometric perception capability while controlling computational resource consumption.
Data Source
AI summary
An image processing method and a related device are disclosed. The method may be applied to a three-dimensional scene in the artificial intelligence field. The method includes: inputting a training sample to a first model to obtain feature information of each image in the training sample, and training the first model. The training sample includes images of a first scene at a plurality of angles of view, including a first image and a second image. An objective of training includes increasing a similarity between first feature information and second feature information, where the first feature information includes feature information of a first point in the first image, the second feature information includes feature information of a second point in the second image, and the first point and the second point correspond to a same point in the first scene.