3D Reconstruction of Remote Sensing Image Using Region Growing Combining with CMVS-PMVS

Wang, Aili; An, Ning; Zhao, Yangyang; Iwahori, Yuji; Kang, Rui

doi:10.14257/ijmue.2016.11.8.03

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…According to the matching graph results, the appropriate initialization image pairs were selected, and the global optimal camera pose parameters were found under the iterative optimization of distributed bundle adjustment. Then, the feature points were triangulated to obtain the sparse model of the three-dimensional point cloud, and the PMVS [23][24][25] algorithm was used for dense reconstruction, thereby improving the efficiency of the three-dimensional reconstruction process of plum from coarse to fine, as well as reducing the running time of the algorithm and the memory occupied by the computer during operation.…”

Section: The 3d Point Cloud Information Construction Of Plummentioning

confidence: 99%

Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

Zhang

Huo

Liu

et al. 2023

Forests

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Rain spots on green plum are superficial micro-defects. Defect detection based on a two-dimensional image is easily influenced by factors such as placement position and light and is prone to misjudgment and omission, which are the main problems affecting the accuracy of defect screening of green plum. In this paper, using computer vision technology, an improved structure from motion (SFM) and patch-based multi-view stereo (PMVS) algorithm based on similar graph clustering and graph matching is proposed to perform three-dimensional sparse and dense reconstruction of green plums. The results show that, compared with the traditional algorithm, the running time of this algorithm is lower, at only 26.55 s, and the mean values of camera optical center error and pose error are 0.019 and 0.631, respectively. This method obtains a higher reconstruction accuracy to meet the subsequent plum micro-defect detection requirements. Aiming at the dense point cloud model of green plums, through point cloud preprocessing, the improved adaptive segmentation algorithm based on the Lab color space realizes the effective segmentation of the point cloud of green plum micro-defects. The experimental results show that the average running time of the improved adaptive segmentation algorithm is 2.56 s, showing a faster segmentation speed and better effect than the traditional K-means and K-means++ algorithms. After clustering the micro-defect point cloud, the micro-defect information of green plums was extracted on the basis of random sample consensus (RANSAC) plane fitting, which provides a theoretical model for further improving the accuracy of sorting the appearance quality of green plums.

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Section: The 3d Point Cloud Information Construction Of Plummentioning

confidence: 99%

Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

Zhang

Huo

Liu

et al. 2023

Forests

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“…Comp Overall Num PMVS [20] 0.613 0.941 0.777 117320 Clomap [2] 0.400 0.664 0.532 1310014 MVSNet [7] 0.396 0.527 0.462 3682198 RMVSNet [8] 0.385 0.459 0.422 5343617 D2HC-RMVSNet [13] 1, it can be seen that comparing the various methods, although the accuracy of this paper's method is not significantly improved compared to other networks, the quality of completeness and integrity is better than that of the above methods, and has a significant advantage. Compared with other deep learning based methods, this paper's method improves accuracy by 3.7%, completeness by 23.9%, and integrity by 14.4% on average, which proves the effectiveness of this paper's method.…”

Section: Accmentioning

confidence: 99%

Multi-View Stereo Matching Method Based on Recursive Neural Networks

2023

jipta

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This article proposes a recursive layered network reconstruction method with pixel-wise attention cost aggregation to address the problems of textureless areas and poor reconstruction results at scene edges in multi-view stereo matching methods. First, multi-scale features of multiple images are extracted through downsampling and transformed into a cost volume using three-dimensional differentiable homography. Then, a pixel-wise attention aggregation module is added to the cost volume aggregation stage to reweight different pixels and generate a new cost volume. Next, a network with recursive layers is used to regularize the cost volume, replacing the traditional 3D CNN network, and an initial depth map is generated. Finally, the filtered and refined depth maps are merged to generate a three-dimensional dense point cloud. Experimental results show that the proposed network model improves completeness, accuracy, and overall quality by 0.377, 0.363, and 0.370, respectively, compared to other network models, and produces more complete point cloud reconstructions in weak texture areas and scene edges.

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“…The authors innovated a classification scheme to lift the classification accuracy. Wang A. et al [11] showed that the point cloud is dense enough which is reconstructed by the 3d reconstruction algorithm based on regional growth combining CMVS-PMVS and well expressed the practical model of object reconstruction; the reconstruction of objects in remote sensing images has very strong practicability, but this algorithm is suitable for a limited number of building types and is not suitable for all types of buildings.…”

Section: Introductionmentioning

confidence: 98%

3D Reconstruction of Optical Building Images Based on Improved 3D-R2N2 Algorithm

2023

Teh. vjesn.

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Three-dimensional reconstruction technology is a key element in the construction of urban geospatial models. Addressing the current shortcomings in reconstruction accuracy, registration results convergence, reconstruction effectiveness, and convergence time of 3D reconstruction algorithms, we propose an optical building object 3D reconstruction method based on an improved 3D-R2N2 algorithm. The method inputs preprocessed optical remote sensing images into a Convolutional Neural Network (CNN) with dense connections for encoding, converting them into a low-dimensional feature matrix and adding a residual connection between every two convolutional layers to enhance network depth. Subsequently, 3D Long Short-Term Memory (3D-LSTM) units are used for transitional connections and cyclic learning. Each unit selectively adjusts or maintains its state, accepting feature vectors computed by the encoder. These data are further passed into a Deep Convolutional Neural Network (DCNN), where each 3D-LSTM hidden unit partially reconstructs output voxels. The DCNN convolutional layer employs an equally sized 3  3  3 convolutional kernel to process these feature data and decode them, thereby accomplishing the 3D reconstruction of buildings. Simultaneously, a pyramid pooling layer is introduced between the feature extraction module and the fully connected layer to enhance the performance of the algorithm. Experimental results indicate that, compared to the 3D-R2N2 algorithm, the SFM-enhanced AKAZE algorithm, the AISI-BIM algorithm, and the improved PMVS algorithm, the proposed algorithm improves the reconstruction effect by 5.3%, 7.8%, 7.4%, and 1.0% respectively. Furthermore, compared to other algorithms, the proposed algorithm exhibits higher efficiency in terms of registration result convergence and reconstruction time, with faster computational speed. This research contributes to the enhancement of building 3D reconstruction technology, laying a foundation for future research in deep learning applications in the architectural field.

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3D Reconstruction of Remote Sensing Image Using Region Growing Combining with CMVS-PMVS

Abstract: Abstract

Cited by 8 publications

References 6 publications

Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

Multi-View Stereo Matching Method Based on Recursive Neural Networks

3D Reconstruction of Optical Building Images Based on Improved 3D-R2N2 Algorithm

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