Map-Relative Pose Regression for Visual Re-Localization
This addresses the problem of efficient visual re-localization for robotics and AR/VR by reducing training data needs and enabling quick adaptation to new scenes, though it is incremental as it builds on existing pose regression frameworks.
The paper tackles the data-hungry and scene-specific training limitations of absolute pose regression networks by introducing map-relative pose regression (marepo), which conditions the regressor on scene-specific maps to enable cross-scene training, achieving superior accuracy on indoor and outdoor datasets compared to previous methods.
Pose regression networks predict the camera pose of a query image relative to a known environment. Within this family of methods, absolute pose regression (APR) has recently shown promising accuracy in the range of a few centimeters in position error. APR networks encode the scene geometry implicitly in their weights. To achieve high accuracy, they require vast amounts of training data that, realistically, can only be created using novel view synthesis in a days-long process. This process has to be repeated for each new scene again and again. We present a new approach to pose regression, map-relative pose regression (marepo), that satisfies the data hunger of the pose regression network in a scene-agnostic fashion. We condition the pose regressor on a scene-specific map representation such that its pose predictions are relative to the scene map. This allows us to train the pose regressor across hundreds of scenes to learn the generic relation between a scene-specific map representation and the camera pose. Our map-relative pose regressor can be applied to new map representations immediately or after mere minutes of fine-tuning for the highest accuracy. Our approach outperforms previous pose regression methods by far on two public datasets, indoor and outdoor. Code is available: https://nianticlabs.github.io/marepo