Camera Pose Estimation Using Vanishing Point Heatmaps
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Solution Overview
Problem
Conventional techniques struggle to accurately estimate vanishing points in distorted images, particularly in urban environments with obstructions like trees, leading to inaccurate camera posture determination.
Innovation Solution
A learning model trained using heatmaps of true vanishing points is employed for pose estimation, enabling accurate estimation of vanishing points and auxiliary diagonal points, which are projected onto a unit sphere to determine the camera's posture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional arc detection methods are used to estimate vanishing points, then the process can be performed without additional sensors, but the estimation accuracy deteriorates in places with obstructions like trees
Solution Approach 1:
The patent replaces conventional geometric arc detection methods with a machine learning-based deep neural network. The neural network directly estimates vanishing points from images, substituting the mechanical/geometric detection process with an AI-based system that can handle complex urban scenes with obstructions more effectively.
Solution Approach 2:
The patent changes the approach from detecting arcs based on geometric assumptions to using a trained neural network model that processes image data directly. This parameter change involves transitioning from traditional computer vision algorithms to deep learning models, enabling better handling of distorted and obstructed views.
2Adaptability or versatility
If arc detection is performed on distorted images, then camera posture can be estimated, but the estimation fails when arcs cannot be detected due to obstructions
Solution Approach 1:
The patent replaces traditional arc-based geometric detection with a deep neural network that directly regresses vanishing point coordinates from image data. This substitution enables the system to handle distorted images and scenes with obstructions more reliably, as the neural network can learn from diverse training data including such challenging conditions.
Solution Approach 2:
The patent uses a trained neural network model that has learned from a large dataset of images with known vanishing points. This copying of knowledge from training data allows the system to generalize to new, unseen scenarios including distorted views and obstructed areas, improving reliability without requiring retraining for each specific condition.
3Measurement precision
If traditional pose estimation methods are used, then the system structure remains simple, but the accuracy of camera posture determination deteriorates
Solution Approach 1:
The patent replaces simple geometric algorithms with a deep neural network architecture that processes images through multiple convolutional layers and pooling operations. This substitution significantly increases computational complexity but achieves superior accuracy in camera posture determination by learning complex patterns from image data.
Solution Approach 2:
The patent employs a pre-trained neural network model that has been trained in advance on a large dataset of images with known vanishing points and camera poses. This preliminary training allows the system to achieve high accuracy without requiring complex real-time computations during actual operation, as the heavy lifting is done during the training phase.
Data Source
AI summary
An information processing apparatus acquires an image taken by a camera and estimates a vanishing point by inputting the image to a trained model, the trained model being generated by subjecting a learning model for pose estimation to machine learning using a heatmap indicative of a true value for the vanishing point as training data.