CryoAlign: feature-based method for global and local 3D alignment of EM density maps

• URL: http://arxiv.org/abs/2309.09217v1
• Date: Sun, 17 Sep 2023 09:07:57 GMT
• Title: CryoAlign: feature-based method for global and local 3D alignment of EM density maps
• Authors: Bintao He, Fa Zhang, Chenjie Feng, Jianyi Yang, Xin Gao and Renmin Han
• Abstract summary: We propose a fast and accurate global and local cryo-electron microscopy density map alignment method CryoAlign. C CryoAlign is the first feature-based EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences.
• Score: 22.748115335755756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advances on cryo-electron imaging technologies have led to a rapidly increasing number of density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we propose a fast and accurate global and local cryo-electron microscopy density map alignment method CryoAlign, which leverages local density feature descriptors to capture spatial structure similarities. CryoAlign is the first feature-based EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences and robust estimation of alignment parameters. Extensive experimental evaluations demonstrate the superiority of CryoAlign over the existing methods in both alignment accuracy and speed.

Related papers

• CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models [20.31346781705925]
  We present CryoLVM, a foundation model that learns rich structural representations from experimental density maps with resolved structures.<n>We demonstrate CryoLVM's effectiveness across three critical cryo-EM tasks: density map sharpening, density map super-resolution, and missing wedge restoration.
  arXiv  Detail & Related papers  (2026-02-02T13:36:36Z)
• ImLoc: Revisiting Visual Localization with Image-based Representation [61.282162006394934]
  We propose to augment each image with estimated depth maps to capture the geometric structure.<n>This representation is easy to build and maintain, but achieves highest accuracy in challenging conditions.<n>Our method achieves a new state-of-the-art accuracy on various standard benchmarks and outperforms existing memory-efficient methods at comparable map sizes.
  arXiv  Detail & Related papers  (2026-01-07T18:51:51Z)
• 3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering [12.85847828490656]
  High-resolution 3D point clouds are highly effective for detecting subtle structural anomalies in industrial inspection.<n>This paper introduces a registration-based anomaly detection framework that combines multi-prototype alignment with cluster-wise discrepancy analysis.<n>Experiments on the Real3D-AD benchmark demonstrate that the proposed method achieves state-of-the-art performance in both object-level and point-level anomaly detection.
  arXiv  Detail & Related papers  (2025-07-17T13:25:17Z)
• PRISM-Loc: a Lightweight Long-range LiDAR Localization in Urban Environments with Topological Maps [0.8009940044669193]
  We propose PRISM-Loc -- a topological map-based approach for localization in large environments.<n>The proposed approach leverages a twofold localization pipeline, which consists of global place recognition and estimation of the local pose inside the found location.<n>We evaluate the proposed method on the ITLP-Campus dataset on a 3 km route, and compare it against the state-of-the-art metric map-based and place recognition-based competitors.
  arXiv  Detail & Related papers  (2025-06-18T19:59:50Z)
• EarthMapper: Visual Autoregressive Models for Controllable Bidirectional Satellite-Map Translation [50.433911327489554]
  We introduce EarthMapper, a novel framework for controllable satellite-map translation. We also contribute CNSatMap, a large-scale dataset comprising 302,132 precisely aligned satellite-map pairs across 38 Chinese cities. experiments on CNSatMap and the New York dataset demonstrate EarthMapper's superior performance.
  arXiv  Detail & Related papers  (2025-04-28T02:41:12Z)
• Map Feature Perception Metric for Map Generation Quality Assessment and Loss Optimization [2.311323886036968]
  This study introduces a novel Map Feature Metric designed to evaluate global characteristics and spatial congruence between synthesized and target maps. Our approach extracts elemental-level deep features that comprehensively encode cartographic structural integrity and topological relationships.
  arXiv  Detail & Related papers  (2025-03-30T09:07:09Z)
• CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets [9.283622646351066]
  Enhancing cryogenic electron microscopy (cryo-EM) 3D density maps at intermediate resolution is crucial in protein structure determination. Recent advances in deep learning have led to the development of automated approaches for enhancing experimental cryo-EM density maps. We propose CryoSAMU, a novel method designed to enhance 3D cryo-EM density maps of protein structures using structure-aware multimodal U-Nets.
  arXiv  Detail & Related papers  (2025-03-26T07:33:36Z)
• EDM: Equirectangular Projection-Oriented Dense Kernelized Feature Matching [49.15373858190824]
  We introduce the first learning-based dense matching algorithm, termed Equirectangular Projection-Oriented Kernelized Feature Matching (EDM) Our proposed method achieves notable performance enhancements, with improvements of +26.72 and +42.62 in AUC@5deg on the Matterport3D and Stanford2D3D datasets.
  arXiv  Detail & Related papers  (2025-02-28T03:37:01Z)
• Volumetric Semantically Consistent 3D Panoptic Mapping [77.13446499924977]
  We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating semantic 3D maps suitable for autonomous agents in unstructured environments. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. The proposed method achieves accuracy superior to the state of the art on public large-scale datasets, improving on a number of widely used metrics.
  arXiv  Detail & Related papers  (2023-09-26T08:03:10Z)
• PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction [72.75478398447396]
  We propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively. Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system. We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane.
  arXiv  Detail & Related papers  (2023-08-31T17:57:17Z)
• View Consistent Purification for Accurate Cross-View Localization [59.48131378244399]
  This paper proposes a fine-grained self-localization method for outdoor robotics. The proposed method addresses limitations in existing cross-view localization methods. It is the first sparse visual-only method that enhances perception in dynamic environments.
  arXiv  Detail & Related papers  (2023-08-16T02:51:52Z)
• A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction [66.21060114843202]
  We propose a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure.
  arXiv  Detail & Related papers  (2023-05-30T13:58:50Z)
• Preserving local densities in low-dimensional embeddings [37.278617643507815]
  State-of-the-art methods, such as tSNE and UMAP, excel in unveiling local structures hidden in high-dimensional data. We show, however, that these methods fail to reconstruct local properties, such as relative differences in densities. We suggest dtSNE, which approximately conserves local densities.
  arXiv  Detail & Related papers  (2023-01-31T16:11:54Z)
• Uniform Manifold Approximation with Two-phase Optimization [13.229510087215552]
  We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO) to improve UMAP. UMATO is a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of high-dimensional data more accurately.
  arXiv  Detail & Related papers  (2022-05-01T08:19:52Z)
• Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching [102.39635336450262]
  We address the problem of ground-to-satellite image geo-localization by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image.
  arXiv  Detail & Related papers  (2022-03-26T20:10:38Z)
• Density-Based Clustering with Kernel Diffusion [59.4179549482505]
  A naive density corresponding to the indicator function of a unit $d$-dimensional Euclidean ball is commonly used in density-based clustering algorithms. We propose a new kernel diffusion density function, which is adaptive to data of varying local distributional characteristics and smoothness.
  arXiv  Detail & Related papers  (2021-10-11T09:00:33Z)
• Joint Characterization of Multiscale Information in High Dimensional Data [0.0]
  We propose a multiscale joint characterization approach designed to exploit synergies between global and local approaches to dimensionality reduction. We show that joint characterization is capable of detecting and isolating signals which are not evident from either PCA or t-sne alone.
  arXiv  Detail & Related papers  (2021-02-18T23:33:00Z)
• Leveraging Planar Regularities for Point Line Visual-Inertial Odometry [13.51108336267342]
  With monocular Visual-Inertial Odometry (VIO) system, 3D point cloud and camera motion can be estimated simultaneously. We propose PLP-VIO, which exploits point features and line features as well as plane regularities. The effectiveness of the proposed method is verified on both synthetic data and public datasets.
  arXiv  Detail & Related papers  (2020-04-16T18:20:00Z)

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.