Structure-Aware Convolution for 3D Point Cloud Classification and Segmentation

Wang, Lei; Liu, Yuxuan; Zhang, Shenman; Yan, Jixing; Tao, Pengjie

doi:10.3390/rs12040634

Cited by 9 publications

(6 citation statements)

References 49 publications

(70 reference statements)

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“…The local geometry was captured by KCNet [12] through the convolution layer of the front-end kernel, but there remained the problem of point-by-point loss. Optimization schemes for extracting local features have been successively improved by the latest research [37][38][39][40][41]. However, the extraction and use of local features still face great challenges.…”

Section: B the Methods Of Deep Learningmentioning

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: B the Methods Of Deep Learningmentioning

confidence: 99%

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

et al. 2021

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“…The formula to calculate the mean Intersection over Union (mIoU) is reported in Equation (6). mIoU, as shown in Figure 11, computes the intersection ratio between ground truth and predicted values per class and averages the sum over the total number of classes K [116].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

Rauch

Braml

2023

Applied Sciences

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Point cloud learning has recently gained strong attention due to its applications in various fields, like computer vision, robotics, and autonomous driving. Point cloud semantic segmentation (PCSS) enables the automatic extraction of semantic information from 3D point cloud data, which makes it a desirable task for construction-related applications as well. Yet, only a limited number of publications have applied deep-learning-based methods to address point cloud understanding for civil engineering problems, and there is still a lack of comprehensive reviews and evaluations of PCSS methods tailored to such use cases. This paper aims to address this gap by providing a survey of recent advances in deep-learning-based PCSS methods and relating them to the challenges of the construction industry. We introduce its significance for the industry and provide a comprehensive look-up table of publicly available datasets for point cloud understanding, with evaluations based on data scene type, sensors, and point features. We address the problem of class imbalance in 3D data for machine learning, provide a compendium of commonly used evaluation metrics for PCSS, and summarize the most significant deep learning methods developed for PCSS. Finally, we discuss the advantages and disadvantages of the methods for specific industry challenges. Our contribution, to the best of our knowledge, is the first survey paper that comprehensively covers deep-learning-based methods for semantic segmentation tasks tailored to construction applications. This paper serves as a useful reference for prospective research and practitioners seeking to develop more accurate and efficient PCSS methods.

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“…Flex convolution [ 21 ] utilizes linear functions to act as a kernel which is actually an order-1 Taylor term of SpiderCNN. Structure-aware Convolution (SAC) [ 22 ] matches neighbor points in the point cloud through 3D convolution to extract geometric features. These convolution works all have significant improvements in several data sets but the training and inference time are much longer than PointNet++ (usually double).…”

Section: Related Workmentioning

confidence: 99%

AttPNet: Attention-Based Deep Neural Network for 3D Point Set Analysis

Yang

Zhang

et al. 2020

Sensors

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Point set is a major type of 3D structure representation format characterized by its data availability and compactness. Most former deep learning-based point set models pay equal attention to different point set regions and channels, thus having limited ability in focusing on small regions and specific channels that are important for characterizing the object of interest. In this paper, we introduce a novel model named Attention-based Point Network (AttPNet). It uses attention mechanism for both global feature masking and channel weighting to focus on characteristic regions and channels. There are two branches in our model. The first branch calculates an attention mask for every point. The second branch uses convolution layers to abstract global features from point sets, where channel attention block is adapted to focus on important channels. Evaluations on the ModelNet40 benchmark dataset show that our model outperforms the existing best model in classification tasks by 0.7% without voting. In addition, experiments on augmented data demonstrate that our model is robust to rotational perturbations and missing points. We also design a Electron Cryo-Tomography (ECT) point cloud dataset and further demonstrate our model’s ability in dealing with fine-grained structures on the ECT dataset.

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Structure-Aware Convolution for 3D Point Cloud Classification and Segmentation

Cited by 9 publications

References 49 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

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

AttPNet: Attention-Based Deep Neural Network for 3D Point Set Analysis

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