Building Plane Matching for City-Scale Camera Localization
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Solution Overview
Problem
Camera localization in large urban environments becomes intractable due to the scale of the scene, as matching against all known 3D points is computationally intensive, and existing instance-based localization methods fail to generalize beyond trained regions, requiring re-learning for new areas.
Innovation Solution
A plane-centric hierarchical localization approach that leverages machine learning to detect building planes, generate embeddings, and use a 3D map for pose determination, incorporating a customized RANSAC algorithm to handle outliers and rectify images for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If matching against all known 3D points is used for camera localization, then localization accuracy can be maintained, but computational complexity becomes intractable at city scale
Solution Approach 1:
The patent segments the city-scale environment into multiple localized 3D maps or scenes. Instead of matching against all 3D points in the entire city, the system divides the large-scale environment into smaller manageable segments, each with its own 3D map. This segmentation reduces the computational burden of feature matching while maintaining localization accuracy within each local region.
Solution Approach 2:
The patent transitions from traditional 2D image-to-3D point matching to plane-based matching, introducing a new dimensional approach. By representing environmental structures as planes (flat surfaces) rather than discrete 3D points, the system creates a more efficient matching space that reduces computational complexity while preserving the essential geometric information needed for accurate localization.
2Adaptability or versatility
If instance-based localization methods are used to leverage geometric primitives, then localization follows human perception better, but the methods fail to generalize beyond trained regions
Solution Approach 1:
The patent creates a universal plane-based representation that can be applied across different regions and environments. The plane detection and matching mechanisms are designed to be region-agnostic, allowing the same localization pipeline to work consistently across city-scale areas without requiring re-training. This universal approach enables the system to generalize reliably to new regions while maintaining alignment with human geometric perception.
3Productivity
If hierarchical approach with image retrieval is used for coarse pose estimation, then computational burden is reduced, but localization accuracy deteriorates compared to full matching
Solution Approach 1:
The patent performs preliminary plane detection and embedding generation across the entire city-scale environment during an offline preprocessing stage. By pre-computing plane embeddings and organizing them in an efficient data structure, the system prepares the groundwork for rapid online localization. This preliminary action enables fast coarse-to-fine search without sacrificing matching accuracy, as the pre-organized plane representations preserve geometric fidelity while enabling efficient retrieval.
Data Source
AI summary
In one embodiment, a method includes accessing an image of one or more target buildings captured by a device; detecting target building planes of the one or more target buildings in the image using a machine learning program; for each of the target building planes: generating a target embedding and target feature points for the target building plane; querying a three-dimensional (3D) map using the target embedding to identify at least one matching building plane for the target building plane; using the target feature points to identify matching feature points of the at least one matching building plane; and determining three-dimensional (3D) locations of the matching feature points using the 3D map; and determining a pose of the device by comparing the target feature points of the target building planes and the 3D locations of the matching feature points associated with the target building planes.