R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization
- URL: http://arxiv.org/abs/2501.01421v2
- Date: Thu, 10 Apr 2025 21:39:39 GMT
- Title: R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization
- Authors: Xudong Jiang, Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys,
- Abstract summary: We introduce a covisibility graph-based global encoding learning and data augmentation strategy.<n>We revisit the network architecture and local feature extraction module.<n>Our method achieves state-of-the-art on challenging large-scale datasets without relying on network ensembles or 3D supervision.
- Score: 66.87005863868181
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Learning-based visual localization methods that use scene coordinate regression (SCR) offer the advantage of smaller map sizes. However, on datasets with complex illumination changes or image-level ambiguities, it remains a less robust alternative to feature matching methods. This work aims to close the gap. We introduce a covisibility graph-based global encoding learning and data augmentation strategy, along with a depth-adjusted reprojection loss to facilitate implicit triangulation. Additionally, we revisit the network architecture and local feature extraction module. Our method achieves state-of-the-art on challenging large-scale datasets without relying on network ensembles or 3D supervision. On Aachen Day-Night, we are 10$\times$ more accurate than previous SCR methods with similar map sizes and require at least 5$\times$ smaller map sizes than any other SCR method while still delivering superior accuracy. Code is available at: https://github.com/cvg/scrstudio .
Related papers
- A-SCoRe: Attention-based Scene Coordinate Regression for wide-ranging scenarios [1.2093553114715083]
A-ScoRe is an Attention-based model which leverage attention on descriptor map level to produce meaningful and high-semantic 2D descriptors.
Results show our methods achieve comparable performance with State-of-the-art methods on multiple benchmark while being light-weighted and much more flexible.
arXiv Detail & Related papers (2025-03-18T07:39:50Z) - An Efficient Scene Coordinate Encoding and Relocalization Method [26.934946734751442]
We propose an efficient scene coordinate encoding and relocalization method.
Compared with the existing SCR methods, we design a unified architecture for both scene encoding and salient keypoint detection.
Comprehensive experiments conducted across indoor and outdoor datasets demonstrate that the proposed system outperforms other state-of-the-art (SOTA) SCR methods.
arXiv Detail & Related papers (2024-12-09T13:39:18Z) - GLACE: Global Local Accelerated Coordinate Encoding [66.87005863868181]
Scene coordinate regression methods are effective in small-scale scenes but face significant challenges in large-scale scenes.
We propose GLACE, which integrates pre-trained global and local encodings and enables SCR to scale to large scenes with only a single small-sized network.
Our method achieves state-of-the-art results on large-scale scenes with a low-map-size model.
arXiv Detail & Related papers (2024-06-06T17:59:50Z) - AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying Scales [45.315661330785275]
We present AGL-NET, a novel learning-based method for global localization using LiDAR point clouds and satellite maps.
We tackle two critical challenges: bridging the representation gap between image and points modalities for robust feature matching, and handling inherent scale discrepancies between global view and local view.
arXiv Detail & Related papers (2024-04-04T04:12:30Z) - Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
We propose a transformer-based deep homography estimation (DHE) network.
It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification.
Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
arXiv Detail & Related papers (2024-02-25T13:22:17Z) - AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation [51.143540967290114]
We propose a method that unlocks a wide range of previously-infeasible geometric augmentations for unsupervised depth computation and estimation.
This is achieved by reversing, or undo''-ing, geometric transformations to the coordinates of the output depth, warping the depth map back to the original reference frame.
arXiv Detail & Related papers (2023-10-15T05:15:45Z) - Quadric Representations for LiDAR Odometry, Mapping and Localization [93.24140840537912]
Current LiDAR odometry, mapping and localization methods leverage point-wise representations of 3D scenes.
We propose a novel method of describing scenes using quadric surfaces, which are far more compact representations of 3D objects.
Our method maintains low latency and memory utility while achieving competitive, and even superior, accuracy.
arXiv Detail & Related papers (2023-04-27T13:52:01Z) - Rethinking Visual Geo-localization for Large-Scale Applications [18.09618985653891]
We build San Francisco eXtra Large, a new dataset covering a whole city and providing a wide range of challenging cases.
We design a new highly scalable training technique, called CosPlace, which casts the training as a classification problem.
We achieve state-of-the-art performance on a wide range of datasets and find that CosPlace is robust to heavy domain changes.
arXiv Detail & Related papers (2022-04-05T15:33:45Z) - Progressive Coordinate Transforms for Monocular 3D Object Detection [52.00071336733109]
We propose a novel and lightweight approach, dubbed em Progressive Coordinate Transforms (PCT) to facilitate learning coordinate representations.
In this paper, we propose a novel and lightweight approach, dubbed em Progressive Coordinate Transforms (PCT) to facilitate learning coordinate representations.
arXiv Detail & Related papers (2021-08-12T15:22:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.