SACReg: Scene-Agnostic Coordinate Regression for Visual Localization

TL;DR

SACReg introduces a scene-agnostic coordinate regression model for visual localization that generalizes across scenes without finetuning, leveraging cross-attention and off-the-shelf retrieval, outperforming scene-specific methods.

Contribution

The paper presents a novel generalized SCR model trained once to work across diverse scenes without scene-specific training or finetuning.

Findings

Outperforms scene-specific models on localization benchmarks.

Uses off-the-shelf image retrieval for scene selection.

Achieves high compression of database representations.

Abstract

Scene coordinates regression (SCR), i.e., predicting 3D coordinates for every pixel of a given image, has recently shown promising potential. However, existing methods remain limited to small scenes memorized during training, and thus hardly scale to realistic datasets and scenarios. In this paper, we propose a generalized SCR model trained once to be deployed in new test scenes, regardless of their scale, without any finetuning. Instead of encoding the scene coordinates into the network weights, our model takes as input a database image with some sparse 2D pixel to 3D coordinate annotations, extracted from e.g. off-the-shelf Structure-from-Motion or RGB-D data, and a query image for which are predicted a dense 3D coordinate map and its confidence, based on cross-attention. At test time, we rely on existing off-the-shelf image retrieval systems and fuse the predictions from a shortlist of relevant database images w.r.t. the query. Afterwards camera pose is obtained using standard Perspective-n-Point (PnP). Starting from selfsupervised CroCo pretrained weights, we train our model on diverse datasets to ensure generalizabilty across various scenarios, and significantly outperform other scene regression approaches, including scene-specific models, on multiple visual localization benchmarks. Finally, we show that the database representation of images and their 2D-3D annotations can be highly compressed with negligible loss of localization performance.