Laplacian and orthogonal wavelet pyramid decompositions in coarse-to-fine registration

As one of China′s most precious cultural relics, the excavation and protection of the Terracotta Warriors pose significant challenges to archaeologists. A fairly common situation in the excavation is that the Terracotta Warriors are mostly found in the form of fragments, and manual reassembly among numerous fragments is laborious and time-consuming. This work presents a fracture-surface-based reassembling method, which is composed of SiamesePointNet, principal component analysis (PCA), and deep closest point (DCP), and is named SPPD. Firstly, SiamesePointNet is proposed to determine whether a pair of point clouds of 3D Terracotta Warrior fragments can be reassembled. Then, a coarse-to-fine registration method based on PCA and DCP is proposed to register the two fragments into a reassembled one. The above two steps iterate until the termination condition is met. A series of experiments on real-world examples are conducted, and the results demonstrate that the proposed method performs better than the conventional reassembling methods. We hope this work can provide a valuable tool for the virtual restoration of three-dimension cultural heritage artifacts.

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A Coarse-to-Fine Registration Approach for Point Cloud Data with Bipartite Graph Structure

Electronics ◽

10.3390/electronics11020263 ◽

2022 ◽

Vol 11 (2) ◽

pp. 263

Author(s):

Munan Yuan ◽

Xiru Li ◽

Longle Cheng ◽

Xiaofeng Li ◽

Haibo Tan

Keyword(s):

Bipartite Graph ◽

Point Cloud ◽

Graph Matching ◽

Data Sets ◽

Graph Structure ◽

Point Cloud Data ◽

Cloud Data ◽

Alignment Process ◽

Coarse To Fine ◽

Fine Registration

Alignment is a critical aspect of point cloud data (PCD) processing, and we propose a coarse-to-fine registration method based on bipartite graph matching in this paper. After data pre-processing, the registration progress can be detailed as follows: Firstly, a top-tail (TT) strategy is designed to normalize and estimate the scale factor of two given PCD sets, which can combine with the coarse alignment process flexibly. Secondly, we utilize the 3D scale-invariant feature transform (3D SIFT) method to extract point features and adopt fast point feature histograms (FPFH) to describe corresponding feature points simultaneously. Thirdly, we construct a similarity weight matrix of the source and target point data sets with bipartite graph structure. Moreover, the similarity weight threshold is used to reject some bipartite graph matching error-point pairs, which determines the dependencies of two data sets and completes the coarse alignment process. Finally, we introduce the trimmed iterative closest point (TrICP) algorithm to perform fine registration. A series of extensive experiments have been conducted to validate that, compared with other algorithms based on ICP and several representative coarse-to-fine alignment methods, the registration accuracy and efficiency of our method are more stable and robust in various scenes and are especially more applicable with scale factors.

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A Novel Coarse-to-fine Registration for 3D Point Cloud Based on Feature Points

DEStech Transactions on Computer Science and Engineering ◽

10.12783/dtcse/cmsam2018/26577 ◽

2018 ◽

Author(s):

Guan-ying HUO ◽

Xin JIANG ◽

Dan-lei YE ◽

Cheng SU ◽

Ze-hong LU ◽

...

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Feature Points ◽

Coarse To Fine ◽

Fine Registration

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Coarse-to-Fine Registration of Remote Sensing Optical Images Using SIFT and SPSA Optimization

Advances in Intelligent Systems and Computing - Soft Computing: Theories and Applications ◽

10.1007/978-981-10-5687-1_69 ◽

2017 ◽

pp. 771-781

Author(s):

Sourabh Paul ◽

Umesh C. Pati

Keyword(s):

Remote Sensing ◽

Optical Images ◽

Coarse To Fine ◽

Fine Registration

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A JOINT EFFORT OF SPEEDED-UP ROBUST FEATURES ALGORITHM AND A DISPARITY-BASED MODEL FOR 3D INDOOR MAPPING USING RGB-D DATA

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702018000300023 ◽

2018 ◽

Vol 24 (3) ◽

pp. 351-366

Author(s):

Marcos Aurélio Basso ◽

Daniel Rodrigues dos Santos

Keyword(s):

Indoor Environments ◽

3D Mapping ◽

Joint Effort ◽

Plane Model ◽

Loop Closure ◽

Speeded Up Robust Features ◽

Speed Up ◽

Coarse To Fine ◽

Fine Registration ◽

Different Levels

Abstract In this paper, we present a method for 3D mapping of indoor environments using RGB-D data. The contribution of our proposed method is two-fold. First, our method exploits a joint effort of the speed-up robust features (SURF) algorithm and a disparity-to-plane model for a coarse-to-fine registration procedure. Once the coarse-to-fine registration task accumulates errors, the same features can appear in two different locations of the map. This is known as the loop closure problem. Then, the variance-covariance matrix that describes the uncertainty of transformation parameters (3D rotation and 3D translation) for view-based loop closure detection followed by a graph-based optimization are proposed to achieve a 3D consistent indoor map. To demonstrate and evaluate the effectiveness of the proposed method, experimental datasets obtained in three indoor environments with different levels of details are used. The experimental results shown that the proposed framework can create 3D indoor maps with an error of 11,97 cm into object space that corresponds to a positional imprecision around 1,5% at the distance of 9 m travelled by sensor.

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A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation

Remote Sensing ◽

10.3390/rs11151833 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1833 ◽

Cited By ~ 5

Author(s):

Han Yang ◽

Xiaorun Li ◽

Liaoying Zhao ◽

Shuhan Chen

Keyword(s):

Remote Sensing ◽

Image Registration ◽

Phase Correlation ◽

Mean Square ◽

Extended Phase ◽

Registration Method ◽

Coarse Registration ◽

Feature Based ◽

Coarse To Fine ◽

Fine Registration

Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results.

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Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2020.2987305 ◽

2020 ◽

Vol 13 ◽

pp. 3125-3144 ◽

Cited By ~ 1

Author(s):

Thanh Huy Nguyen ◽

Sylvie Daniel ◽

Didier Gueriot ◽

Christophe Sintes ◽

Jean-Marc Le Caillec

Keyword(s):

Airborne Lidar ◽

Lidar Data ◽

Urban Scenes ◽

Optical Imagery ◽

Airborne Lidar Data ◽

Coarse To Fine ◽

Fine Registration

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A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences

Remote Sensing ◽

10.3390/rs11040470 ◽

2019 ◽

Vol 11 (4) ◽

pp. 470 ◽

Cited By ~ 3

Author(s):

Kai Li ◽

Yongsheng Zhang ◽

Zhenchao Zhang ◽

Guangling Lai

Keyword(s):

Reference Image ◽

Registration Accuracy ◽

Registration Method ◽

Normalized Mutual Information ◽

Sensing Applications ◽

Validation Experiment ◽

Image Pairs ◽

Coarse To Fine ◽

Fine Registration ◽

Three Phases

Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times.

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