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PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
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TLDR
Experimental results show that the PointAtrousGraph (PAG) outperform previous state-of-the-art methods on various 3D semantic perception applications.Abstract:
Motivated by the success of encoding multi-scale contextual information for image analysis, we propose our PointAtrousGraph (PAG) - a deep permutation-invariant hierarchical encoder-decoder for efficiently exploiting multi-scale edge features in point clouds. Our PAG is constructed by several novel modules, such as Point Atrous Convolution (PAC), Edge-preserved Pooling (EP) and Edge-preserved Unpooling (EU). Similar with atrous convolution, our PAC can effectively enlarge receptive fields of filters and thus densely learn multi-scale point features. Following the idea of non-overlapping max-pooling operations, we propose our EP to preserve critical edge features during subsampling. Correspondingly, our EU modules gradually recover spatial information for edge features. In addition, we introduce chained skip subsampling/upsampling modules that directly propagate edge features to the final stage. Particularly, our proposed auxiliary loss functions can further improve our performance. Experimental results show that our PAG outperform previous state-of-the-art methods on various 3D semantic perception applications.read more
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Journal ArticleDOI
Deep Learning for 3D Point Clouds: A Survey
TL;DR: This paper presents a comprehensive review of recent progress in deep learning methods for point clouds, covering three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.
Posted Content
Deep Learning for 3D Point Clouds: A Survey
TL;DR: Wang et al. as mentioned in this paper presented a comprehensive review of recent progress in deep learning methods for point clouds, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.
Journal ArticleDOI
Scanning Technologies to Building Information Modelling: A Review
TL;DR: In this article , the authors present a review of the Scan-to-BIM (S2BIM) methodology and different methods involved in point-cloud processing such as sampling, registration and semantic segmentation.
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DCG-Net: Dynamic Capsule Graph Convolutional Network for Point Clouds
Dena Bazazian,Dhananjay Nahata +1 more
TL;DR: DCG-Net (Dynamic Capsule Graph Network) is introduced to analyze point clouds for the tasks of classification and segmentation based on the dynamic routing mechanism of capsule networks at each layer of a convolutional network.
Journal ArticleDOI
PU-GACNet: Graph Attention Convolution Network for Point Cloud Upsampling
Bing Han,Xinyun Zhang,Shuang Ren +2 more
TL;DR: Zhang et al. as mentioned in this paper designed a Graph Attention Convolution (GAC) module as a feature extractor by assigning different attentional weights to combine spatial positions and feature attributes dynamically.
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Proceedings Article
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