Related papers: Spatial regularisation for improved accuracy and interpretability in keypoint-based registration

Spatial regularisation for improved accuracy and interpretability in keypoint-based registration

URL: http://arxiv.org/abs/2503.04499v2
Date: Fri, 07 Mar 2025 15:51:19 GMT
Title: Spatial regularisation for improved accuracy and interpretability in keypoint-based registration
Authors: Benjamin Billot, Ramya Muthukrishnan, Esra Abaci-Turk, P. Ellen Grant, Nicholas Ayache, Hervé Delingette, Polina Golland,
Abstract summary: Recent approaches based on unsupervised keypoint detection stand out as very promising for interpretability.<n>Here, we propose a three-fold loss to regularise the spatial distribution of the features.<n>Our loss considerably improves the interpretability of the features, which now correspond to precise and anatomically meaningful landmarks.
Score: 5.286949071316761
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Unsupervised registration strategies bypass requirements in ground truth transforms or segmentations by optimising similarity metrics between fixed and moved volumes. Among these methods, a recent subclass of approaches based on unsupervised keypoint detection stand out as very promising for interpretability. Specifically, these methods train a network to predict feature maps for fixed and moving images, from which explainable centres of mass are computed to obtain point clouds, that are then aligned in closed-form. However, the features returned by the network often yield spatially diffuse patterns that are hard to interpret, thus undermining the purpose of keypoint-based registration. Here, we propose a three-fold loss to regularise the spatial distribution of the features. First, we use the KL divergence to model features as point spread functions that we interpret as probabilistic keypoints. Then, we sharpen the spatial distributions of these features to increase the precision of the detected landmarks. Finally, we introduce a new repulsive loss across keypoints to encourage spatial diversity. Overall, our loss considerably improves the interpretability of the features, which now correspond to precise and anatomically meaningful landmarks. We demonstrate our three-fold loss in foetal rigid motion tracking and brain MRI affine registration tasks, where it not only outperforms state-of-the-art unsupervised strategies, but also bridges the gap with state-of-the-art supervised methods. Our code is available at https://github.com/BenBillot/spatial_regularisation.

Related papers

REGRACE: A Robust and Efficient Graph-based Re-localization Algorithm using Consistency Evaluation [23.41000678070751]
Loop closures are essential for correcting odometry drift and creating consistent maps.<n>Current methods using dense point clouds for accurate place recognition do not scale well due to computationally expensive scan-to-scan comparisons.<n>We introduce REGRACE, a novel approach that addresses these challenges of scalability and perspective difference in re-localization.
arXiv Detail & Related papers (2025-03-05T15:32:38Z)
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation [51.66997548477913]
We propose a novel feature-level consistency learning framework named Density-Descending Feature Perturbation (DDFP) Inspired by the low-density separation assumption in semi-supervised learning, our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore. The proposed DDFP outperforms other designs on feature-level perturbations and shows state of the art performances on both Pascal VOC and Cityscapes dataset.
arXiv Detail & Related papers (2024-03-11T06:59:05Z)
Learning Feature Matching via Matchable Keypoint-Assisted Graph Neural Network [52.29330138835208]
Accurately matching local features between a pair of images is a challenging computer vision task. Previous studies typically use attention based graph neural networks (GNNs) with fully-connected graphs over keypoints within/across images. We propose MaKeGNN, a sparse attention-based GNN architecture which bypasses non-repeatable keypoints and leverages matchable ones to guide message passing.
arXiv Detail & Related papers (2023-07-04T02:50:44Z)
Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration [74.53755415380171]
Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data. We propose UDPReg, an unsupervised deep probabilistic registration framework for point clouds with partial overlaps. Our UDPReg achieves competitive performance on the 3DMatch/3DLoMatch and ModelNet/ModelLoNet benchmarks.
arXiv Detail & Related papers (2023-03-23T14:18:06Z)
Large-scale Point Cloud Registration Based on Graph Matching Optimization [30.92028761652611]
We propose a underlineGraph underlineMatching underlineOptimization based underlineNetwork. The proposed method has been evaluated on the 3DMatch/3DLoMatch benchmarks and the KITTI benchmark.
arXiv Detail & Related papers (2023-02-12T03:29:35Z)
Regressive Domain Adaptation for Unsupervised Keypoint Detection [67.2950306888855]
Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. We present a method of regressive domain adaptation (RegDA) for unsupervised keypoint detection. Our method brings large improvement by 8% to 11% in terms of PCK on different datasets.
arXiv Detail & Related papers (2021-03-10T16:45:22Z)
FFD: Fast Feature Detector [22.51804239092462]
We show that robust and accurate keypoints exist in the specific scale-space domain. It is proved that setting the scale-space pyramid's smoothness ratio and blurring to 2 and 0.627, respectively, facilitates the detection of reliable keypoints.
arXiv Detail & Related papers (2020-12-01T21:56:35Z)
Unsupervised Metric Relocalization Using Transform Consistency Loss [66.19479868638925]
Training networks to perform metric relocalization traditionally requires accurate image correspondences. We propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.
arXiv Detail & Related papers (2020-11-01T19:24:27Z)
BRUL\`E: Barycenter-Regularized Unsupervised Landmark Extraction [2.2758845733923687]
Unsupervised retrieval of image features is vital for many computer vision tasks where the annotation is missing or scarce. We propose a new unsupervised approach to detect the landmarks in images, validating it on the popular task of human face key-points extraction. The method is based on the idea of auto-encoding the wanted landmarks in the latent space while discarding the non-essential information.
arXiv Detail & Related papers (2020-06-20T20:04:00Z)
ASLFeat: Learning Local Features of Accurate Shape and Localization [42.70030492742363]
We present ASLFeat, with three light-weight yet effective modifications to mitigate above issues. First, we resort to deformable convolutional networks to densely estimate and apply local transformation. Second, we take advantage of the inherent feature hierarchy to restore spatial resolution and low-level details for accurate keypoint localization.
arXiv Detail & Related papers (2020-03-23T04:03:03Z)
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation [72.40827239394565]
We propose to compute features only at sparsely sampled locations. We then densely reconstruct the feature map with an efficient procedure. The presented network is experimentally shown to save substantial computation while maintaining accuracy over a variety of computer vision tasks.
arXiv Detail & Related papers (2020-03-19T15:36:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.