Mesh Denoising via Adaptive Consistent Neighborhood

Guo, Mingqiang; Song, Zhenzhen; Han, Chengde; Su, Zhong; Lv, Ruina; Liu, Zheng

doi:10.3390/s21020412

Cited by 9 publications

(5 citation statements)

References 42 publications

(48 reference statements)

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“…This work was compared with some new methods, including FCN [31], SegNet [30], DilatedNet [46], U-Net [21], PSPNet [22], DeepLab series [23,24,47], and Mask R-CNN [48], to evaluate the effectiveness of the proposed ME-Net model in mangrove extraction from remote sensing imagery. All methods were trained, validated, and tested on the same datasets for an objective and impartial finding.…”

Section: Model Analysismentioning

confidence: 99%

“…Although some over-fitting cases were found, the overall performance of ME-Net model in mangrove extraction was still much better than that of the other pixel classification models. In addition, the noise in the remote sensing imagery will decrease the accuracy of ME-Net, and the denoising method will be exploited to address this problem in the future [48,49]. Some scenes, which were difficult to classify, such as nonblock, sporadic scattered, and coastal strip edges, were used in the experiments to increase the contrast of the classification results.…”

Section: Model Analysismentioning

confidence: 99%

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ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

Guo

et al. 2021

Remote Sensing

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Mangroves play an important role in many aspects of ecosystem services. Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area. However, popular mangrove extraction methods, such as the object-oriented method, still have some defects for remote sensing imagery, such as being low-intelligence, time-consuming, and laborious. A pixel classification model inspired by deep learning technology was proposed to solve these problems. Three modules in the proposed model were designed to improve the model performance. A multiscale context embedding module was designed to extract multiscale context information. Location information was restored by the global attention module, and the boundary of the feature map was optimized by the boundary fitting unit. Remote sensing imagery and mangrove distribution ground truth labels obtained through visual interpretation were applied to build the dataset. Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove. Finally, comparative experiments were conducted to prove the potential for mangrove extraction. We selected the Sentinel-2A remote sensing data acquired on 13 April 2018 in Hainan Dongzhaigang National Nature Reserve in China to conduct a group of experiments. After processing, the data exhibited 2093 × 2214 pixels, and a mangrove extraction dataset was generated. The dataset was made from Sentinel-2A satellite, which includes five original bands, namely R, G, B, NIR, and SWIR-1, and six multispectral indices, namely normalization difference vegetation index (NDVI), modified normalized difference water index (MNDWI), forest discrimination index (FDI), wetland forest index (WFI), mangrove discrimination index (MDI), and the first principal component (PCA1). The dataset has a total of 6400 images. Experimental results based on datasets show that the overall accuracy of the trained mangrove extraction network reaches 97.48%. Our method benefits from CNN and achieves a more accurate intersection and union ratio than other machine learning and pixel classification methods by analysis. The designed model global attention module, multiscale context embedding, and boundary fitting unit are helpful for mangrove extraction.

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Section: Model Analysismentioning

confidence: 99%

Section: Model Analysismentioning

confidence: 99%

ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

Guo

et al. 2021

Remote Sensing

Self Cite

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“…These two methods can keep sharp geometric features while removing noise effectively. However, both of them inevitably suffer from serious staircase artifacts in smooth transition regions [34][35][36][37][38][39][40]. For alleviating these artifacts, Liu et al [41] have recently introduced a point cloud denoising framework, which presents an anisotropic second-order regularizer to remove noise and preserve sharp geometric features as well as smooth transition regions.…”

Section: Related Workmentioning

confidence: 99%

A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information

Guo

et al. 2021

Remote Sensing

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Rigid registration of 3D indoor scenes is a fundamental yet vital task in various fields that include remote sensing (e.g., 3D reconstruction of indoor scenes), photogrammetry measurement, geometry modeling, etc. Nevertheless, state-of-the-art registration approaches still have defects when dealing with low-quality indoor scene point clouds derived from consumer-grade RGB-D sensors. The major challenge is accurately extracting correspondences between a pair of low-quality point clouds when they contain considerable noise, outliers, or weak texture features. To solve the problem, we present a point cloud registration framework in view of RGB-D information. First, we propose a point normal filter for effectively removing noise and simultaneously maintaining sharp geometric features and smooth transition regions. Second, we design a correspondence extraction scheme based on a novel descriptor encoding textural and geometry information, which can robustly establish dense correspondences between a pair of low-quality point clouds. Finally, we propose a point-to-plane registration technology via a nonconvex regularizer, which can further diminish the influence of those false correspondences and produce an exact rigid transformation between a pair of point clouds. Compared to existing state-of-the-art techniques, intensive experimental results demonstrate that our registration framework is excellent visually and numerically, especially for dealing with low-quality indoor scenes.

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“…Our goal is different since we focus on feature classification and preservation more effectively. The authors in [34] introduced a two-stage scheme to construct adaptive consistent neighborhoods for guided normal filtering. The authors in [2] proposed further choosing a more consistent sub-patch to estimate the guidance normal.…”

Section: Anisotropic Mesh Filteringmentioning

confidence: 99%

3D Mesh Pre-Processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

et al. 2021

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3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.

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Mesh Denoising via Adaptive Consistent Neighborhood

Cited by 9 publications

References 42 publications

ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information

3D Mesh Pre-Processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

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