Map-Relative Pose Regression for Data-Efficient Visual Relocalization
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Solution Overview
Problem
Conventional absolute pose regression (APR) networks for camera pose estimation require vast amounts of training data and computing resources, leading to a slow and resource-intensive training process that is not efficient.
Innovation Solution
A pose estimation network comprising a scene-specific scene geometry prediction network and a scene-agnostic, map-relative pose regressor that predicts camera pose relative to a scene map, allowing training across multiple scenes and enabling efficient pose estimation without depth maps or point clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional absolute pose regression networks are trained to achieve high accuracy, then position error is reduced to a few centimeters, but training data storage requirements and computing resources increase significantly
Solution Approach 1:
The system divides the pose regression task into two independent components: a scene-specific geometry prediction network that processes input images to generate scene representations, and a scene-agnostic pose regressor that maps these representations to camera poses. This segmentation allows each component to be optimized independently, with the geometry network capturing scene-specific features and the pose regressor learning universal pose relationships from diverse scenes, thereby reducing the training data burden while maintaining accuracy.
Solution Approach 2:
The patent introduces scene coordinate maps as an intermediary representation between the input image and the pose prediction. The geometry prediction network generates these coordinate maps that encode scene geometry information, which then serve as input to the pose regressor. This intermediary abstraction allows the pose regressor to operate on compact geometric representations rather than raw images, reducing computational complexity and data requirements while preserving essential geometric cues for accurate pose estimation.
2Measurement precision
If conventional absolute pose regression networks are trained to achieve high accuracy, then pose estimation precision is improved, but training time and computational resources increase significantly
Solution Approach 1:
By segmenting the network into a geometry prediction component and a pose regression component, the system enables parallel training of specialized sub-networks. The scene-agnostic pose regressor can be trained on diverse scene data once and reused across multiple applications, eliminating the need for repeated full-network training for each new scene. This segmentation dramatically reduces total training time while maintaining high pose estimation precision through the specialized geometry network.
Solution Approach 2:
The geometry prediction network performs preliminary processing of input images to generate scene coordinate maps before pose estimation occurs. This preliminary action extracts and encodes essential geometric features in advance, allowing the pose regressor to operate on pre-processed, compact representations rather than raw high-dimensional images. This preliminary feature extraction accelerates both training and inference while preserving the precision needed for accurate pose estimation.
3Measurement precision
If vast amounts of training data are used to train pose regression networks, then accuracy is improved, but storage resources and computational overhead increase
Solution Approach 1:
The patent extracts and separates the scene-specific geometry understanding from the scene-agnostic pose regression task. The geometry prediction network extracts scene-specific spatial relationships and generates coordinate maps that encapsulate essential geometric information. This extraction allows the pose regressor to operate on compact, processed representations rather than requiring access to vast amounts of raw training data, thereby reducing storage requirements and system complexity while maintaining accuracy.
Solution Approach 2:
The system transforms the input representation from raw high-dimensional images to compact scene coordinate maps generated by the geometry network. This parameter transformation condenses the essential geometric information into a lower-dimensional space that is more efficient for pose regression. By changing the representation parameters from pixels to structured coordinate information, the system reduces computational overhead and storage requirements while preserving the accuracy needed for precise pose prediction.
Data Source
AI summary
This disclosure pertains to a scene-agnostic, map-relative pose regression method. The pose regressor is conditioned on a scene-specific map representation such that its pose predictions are relative to the scene map. This allows training of the pose regressor across multiple scenes to learn the generic relation between a scene-specific map representation and the camera pose. The map-relative pose regressor can then be applied to new map representations.