Developing a Semantic-Driven Hybrid Segmentation Method for Point Clouds of 3D Shapes

Yang, Xia; Li, Qingde; He, Ligang; Pang, Mingjun; Jia, Caiqin

doi:10.1109/access.2020.2976847

Cited by 4 publications

(2 citation statements)

References 59 publications

(87 reference statements)

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“…The extraction of edges and sharp features was completed by clustering [15], but for a model with many curve features, the extracted features had defects. There have also been improvements based on semantic level [16,17]. However, there were some segmentation obstacles for local features of concavo-convexity.…”

Section: A Traditional Methodsmentioning

confidence: 99%

MLFNet- Point Cloud Semantic Segmentation Convolution Network Based on Multi-Scale Feature Fusion

et al. 2021

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The project was funded by jinan science and technology bureau and undertaken by QiluUniversity of Technology(Shandong Academy of Sciences),Machine vision-based online intelligentsegmentation of foreign objects in liquids is implemented.2019GXRC067. ABSTRACTIn the semantic segmentation of a point cloud, if the spatial structure correlation between the input features and coordinates are not fully considered, a semantic segmentation error can occur. We propose a method of spatial convolution that makes full use of the characteristics of a multiscale spatial structure by combining local and global features. We call this method MLFNet. We also propose a multiscale feature framework. First, the point cloud is simplified by obtaining the weighted farthest point (by down-sampling combined with farthest-point sampling and the weighted average). The near-near domain of each sampling point is then obtained by a KK octant search (an octant search optimized by the knearest neighbor and a custom threshold), and feature information is obtained. The feature information is added to the subsequent multilayer perceptron, and fusion of local context information is achieved. Finally, the fusion features in multiple directions are maximally pooled. Our method was tested on self-made datasets and other standard basic datasets (ModelNet40, ShapeNet, and Stanford large-scale 3D indoor spaces (S3DIS) data). The accuracy of segmentation was 0.937 in our dataset; two percentage points higher than the latest deep learning method. Also, our method obtained a mean intersection over a union of 0.867 in ShapeNet, which was 0.3 percentage points higher than the latest PointGrid. The accuracy on S3DIS was 0.8153, which was three percentage points higher than the latest spatial aggregation net. The results of semantic segmentation verified the superiority of the proposed method.

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Section: A Traditional Methodsmentioning

confidence: 99%

MLFNet- Point Cloud Semantic Segmentation Convolution Network Based on Multi-Scale Feature Fusion

et al. 2021

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“…An object such as mug and teapot in Fig. 6 can be segmented first so that the handle and the main body are separated using the method in [31]. Then the main body is independently deformed using the method presented above.…”

Section: Deformationmentioning

confidence: 99%

Developing an Image-Based 3D Model Editing Method

Pang

Xiong

et al. 2020

IEEE Access

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As 3D technologies advance rapidly, 3D printing, 3D animation, and 3D Movie are springing out in different areas. It becomes a remarkable challenge to generate a large number of 3D models effectively and efficiently. This paper proposes a novel editing method based on the feature lines of images (i.e., image contour and principal axis) for generating new 3D models. Our method takes as input an existing 3D model (as the original model) and an image selected by the user (or a sketch hand-drawn by the users), and performs the model editing to generate a new 3D model. In particular, our method first takes as input an original 3D model and an image selected by the user. Second, the selected image is processed to produce the feature lines, i.e., contour and principal axis of the image. Third, the silhouette of the original model from a given view is acquired, and projected on a projection plane to produce a contour and principal axis of the model, which is the feature lines of the original model. Fourth, by comparing the feature lines of the image and the original model, the constraint conditions are established to control the editing of the 3D model. Finally, 3D model editing is conducted through the as-rigid-as-possible mesh deformation to produce a new 3D model with the appearance resembling the selected image. Furthermore, this paper proposes an energy function to guide the detailed model editing, and measure the similarity between the generated 3D model and the corresponding image. We have conducted extensive experiments to evaluate the proposed method. The results show that comparing with the existing editing methods in literature, the proposed model editing method is able to construct various types of 3D models more effectively and more efficiently.

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Weakly Supervised Learning Model for Clustering and Segmentation of 3D Point on Cloud Shape Data

Athawale

Soni

Murthy

et al. 2023

Lecture Notes in Networks and Systems

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Developing a Semantic-Driven Hybrid Segmentation Method for Point Clouds of 3D Shapes

Cited by 4 publications

References 59 publications

MLFNet- Point Cloud Semantic Segmentation Convolution Network Based on Multi-Scale Feature Fusion

MLFNet- Point Cloud Semantic Segmentation Convolution Network Based on Multi-Scale Feature Fusion

Developing an Image-Based 3D Model Editing Method

Weakly Supervised Learning Model for Clustering and Segmentation of 3D Point on Cloud Shape Data

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