Camera Pose Estimation via Patch-Based Neural Network Prediction
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Solution Overview
Problem
Existing methods for estimating the pose of a camera in a 3D scene are complex, require significant computing resources, need prior calibration, and are not suitable for embedded virtual reality applications due to the requirement of depth cameras and complexity in determining scaling factors.
Innovation Solution
A method that predicts the 3D localization of small image patches using a neural network and decision random forest, reducing complexity by focusing on patch-based predictions, eliminating the need for depth maps and prior calibration, and providing a confidence measure for pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If point cloud manipulation is used for pose estimation, then automation is improved, but device complexity increases
Solution Approach 1:
The patent segments the image into multiple patches, each processed independently by the neural network to predict local 3D positions. This divides the complex global pose estimation problem into simpler local predictions, reducing implementation complexity while maintaining automation.
2Measurement precision
If prior calibration is performed to account for camera intrinsic parameters, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The patent performs preliminary action by training the neural network on a large dataset of image patches with associated 3D positions during the learning phase. This pre-learning embeds camera intrinsic parameters and geometric relationships into the network weights, eliminating the need for manual calibration during operation while maintaining high precision.
3Measurement precision
If depth camera is used in test phase, then measurement precision is improved, but adaptability worsens
Solution Approach 1:
The patent creates a virtual 3D representation by predicting 3D positions of image patches from 2D RGB images alone, copying the functionality of depth cameras through neural network inference. This eliminates the need for physical depth sensors while achieving comparable 3D reconstruction capability, enabling deployment on mobile devices.
4Measurement precision
If scaling factor is included in loss function, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent enables the system to self-adjust the scaling factor during training by incorporating it directly into the loss function. The neural network automatically learns the appropriate scaling from the data without requiring manual configuration or empirical tuning, eliminating the complexity of determining optimal scaling factors while maintaining precision.
Data Source
AI summary
A method for estimating a camera pose in a frame of reference of a three-dimensional scene, including: obtaining an image of colour intensities of the scene; extracting points of interest, which are invariant by geometric transformation of the image; forming patches in the image, each including an extracted point of interest; predicting 3D locations of the points of interest by applying an automatic prediction system, trained by a training set including patches from images acquired from plural points of view, an image being associated with a 2D position of its point of interest in a frame of reference of the image and with a 3D position of its point of interest in the frame of reference of the scene; estimating the pose of a camera, by mapping the 2D positions of the points of interest and reprojections in the current image frame of reference of the predicted 3D locations.