Bokeh-Based Distance Estimation Model Training
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Solution Overview
Problem
Generating high-accuracy statistical models for estimating distances using monocular cameras is challenging due to the difficulty in preparing large datasets, especially in varying environments, as existing methods require extensive data collection and precise distance measurements.
Innovation Solution
A learning method that utilizes a statistical model to predict bokeh values from images captured by a monocular camera, where the model is trained using multi-view images from different viewpoints, allowing for online learning and adaptation to new environments without requiring explicit distance labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a statistical model is trained using traditional methods with explicit distance labels, then measurement precision may be improved, but data preparation complexity and time increase significantly
Solution Approach 1:
The patent creates virtual distance labels by copying depth information from pre-trained depth estimation models or structure-from-motion algorithms, avoiding the need for manual measurement while maintaining training data quality
Solution Approach 2:
The patent introduces an intermediary depth prediction model that generates pseudo-labels for training the bokeh-based distance estimation model, bridging the gap between available data and required training labels
2Measurement precision
If a statistical model is trained on domain-specific data, then measurement precision improves for that domain, but adaptability to new environments deteriorates
Solution Approach 1:
The patent trains the statistical model on multi-domain data including indoor and outdoor environments, making the model universally applicable across different settings rather than specialized for a single domain
Solution Approach 2:
The patent incorporates domain adaptation techniques that adjust model parameters based on environmental characteristics, allowing the model to adapt to new domains while maintaining core learning
3Measurement precision
If extensive distance measurements are collected for training, then measurement precision improves, but device complexity and measurement difficulty increase
Solution Approach 1:
The patent enables the system to generate its own training labels automatically through computational methods, eliminating the need for external measurement devices or manual annotation processes
Data Source
AI summary
According to one embodiment, a learning method for causing a statistical model to learn is provided. The statistical model is generated by learning a bokeh caused in a first image captured in a first domain in accordance with a distance to a first subject included in the first image, the method includes acquiring a plurality of second images by capturing a second subject from multiple viewpoints in a second domain other than the first domain, and causing the statistical model to learn using the second images.