Camera Localization Using Learned 3D Landmarks
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Solution Overview
Problem
Camera localization in resource-constrained devices is challenging due to the need for long-term storage of scene images or features, which raises privacy concerns and exceeds memory limits.
Innovation Solution
A learned camera localization method that encodes pre-specified 3D scene landmarks into a machine learning model, allowing for accurate detection and prediction of 2D image locations and 3D bearing vectors without storing image features or 3D point clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scene images or features are stored for camera localization, then localization accuracy is improved, but memory consumption increases and privacy concerns arise
Solution Approach 1:
The patent extracts only the essential 3D geometric information (landmark positions and bearing vectors) from the complete scene data, storing merely the sparse 3D point cloud of landmarks rather than full scene images or dense feature sets. This extraction approach maintains localization accuracy while dramatically reducing memory requirements.
Solution Approach 2:
Instead of storing 2D image features and projecting them to 3D, the patent inverts the approach by pre-building a 3D map of landmark positions and then projecting these 3D points to 2D image locations during localization. This inversion eliminates the need to store large amounts of 2D image data while preserving localization capability.
2Measurement precision
If scene images or features are stored for camera localization, then localization accuracy is improved, but privacy concerns arise
Solution Approach 1:
The patent extracts only anonymous 3D geometric landmarks from the scene, deliberately excluding any stored image data, textures, or identifiable features. This extraction of purely geometric information maintains localization functionality while eliminating privacy risks associated with storing visual scene data.
3Reliability
If traditional feature matching methods are used for camera localization, then robustness is improved, but device complexity increases
Solution Approach 1:
The patent replaces the traditional mechanical feature matching pipeline (detection, description, matching, verification) with a direct geometric projection approach. By substituting the complex feature matching mechanism with simple 2D-3D point projection and bearing vector computation, the system achieves comparable robustness with significantly reduced complexity suitable for mobile devices.
Data Source
AI summary
In various embodiments there is a method for camera localization within a scene. An image of a scene captured by the camera is input to a machine learning model, which has been trained for the particular scene to detect a plurality of 3D scene landmarks. The 3D scene landmarks are pre-specified in a pre-built map of the scene. The machine learning model outputs a plurality of predictions, each prediction comprising: either a 2D location in the image which is predicted to depict one of the 3D scene landmarks, or a 3D bearing vector, being a vector originating at the camera and pointing towards a predicted 3D location of one of the 3D scene landmarks. Using the predictions, an estimate of a position and orientation of the camera in the pre-built map of the scene is computed.