scholarly journals Generating Point Cloud from Measurements and Shapes Based on Convolutional Neural Network: An Application for Building 3D Human Model

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Mau Tung Nguyen ◽  
Thanh Vu Dang ◽  
Minh Kieu Tran Thi ◽  
Pham The Bao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Data Representation ◽  
Human Model ◽  
Average Error ◽  
Shape Information ◽  
Hierarchical Learning ◽  
3D Data ◽  
3D Shapes

It has been widely known that 3D shape models are comprehensively parameterized using point cloud and meshes. The point cloud particularly is much simpler to handle compared with meshes, and it also contains the shape information of a 3D model. In this paper, we would like to introduce our new method to generating the 3D point cloud from a set of crucial measurements and shapes of importance positions. In order to find the correspondence between shapes and measurements, we introduced a method of representing 3D data called slice structure. A Neural Networks-based hierarchical learning model is presented to be compatible with the data representation. Primary slices are generated by matching the measurements set before the whole point cloud tuned by Convolutional Neural Network. We conducted the experiment on a 3D human dataset which contains 1706 examples. Our results demonstrate the effectiveness of the proposed framework with the average error 7.72% and fine visualization. This study indicates that paying more attention to local features is worthwhile when dealing with 3D shapes.

scholarly journals SEMANTIC SEGMENTATION OF INDOOR POINT CLOUDS USING CONVOLUTIONAL NEURAL NETWORK

2017 ◽  
Vol IV-4/W4 ◽  
pp. 101-108 ◽  
Author(s):  
K. Babacan ◽  
L. Chen ◽  
G. Sohn
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Point Cloud ◽  
Semantic Information ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Semantic Content ◽  
Data Representation ◽  
Cloud Data

As Building Information Modelling (BIM) thrives, geometry becomes no longer sufficient; an ever increasing variety of semantic information is needed to express an indoor model adequately. On the other hand, for the existing buildings, automatically generating semantically enriched BIM from point cloud data is in its infancy. The previous research to enhance the semantic content rely on frameworks in which some specific rules and/or features that are hand coded by specialists. These methods immanently lack generalization and easily break in different circumstances. On this account, a generalized framework is urgently needed to automatically and accurately generate semantic information. Therefore we propose to employ deep learning techniques for the semantic segmentation of point clouds into meaningful parts. More specifically, we build a volumetric data representation in order to efficiently generate the high number of training samples needed to initiate a convolutional neural network architecture. The feedforward propagation is used in such a way to perform the classification in voxel level for achieving semantic segmentation. The method is tested both for a mobile laser scanner point cloud, and a larger scale synthetically generated data. We also demonstrate a case study, in which our method can be effectively used to leverage the extraction of planar surfaces in challenging cluttered indoor environments.

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

Ship Classification Method for Point Cloud Images Based on Three-Dimensional Convolutional Neural Network

2020 ◽  
Vol 57 (16) ◽  
pp. 161022
Author(s):  
任永梅 Ren Yongmei ◽  
杨杰 Yang Jie ◽  
郭志强 Guo Zhiqiang ◽  
陈奕蕾 Chen Yilei
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Three Dimensional ◽  
Classification Method ◽  
Ship Classification

Point Cloud Registration Based on Convolutional Neural Network

2017 ◽  
Vol 54 (3) ◽  
pp. 031001 ◽  
Author(s):  
舒程珣 Shu Chengxun ◽  
何云涛 He Yuntao ◽  
孙庆科 Sun Qingke
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Point Cloud Registration

scholarly journals MeshNet: Mesh Neural Network for 3D Shape Representation

2019 ◽  
Vol 33 ◽  
pp. 8279-8286 ◽  
Author(s):  
Yutong Feng ◽  
Yifan Feng ◽  
Haoxuan You ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Point Cloud ◽  
State Of The Art ◽  
Shape Representation ◽  
Shape Classification ◽  
Retrieval Performance ◽  
3D Shape ◽  
General Architecture ◽  
3D Shapes

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation.

scholarly journals An overview of ongoing point cloud compression standardization activities: video-based (V-PCC) and geometry-based (G-PCC)

2020 ◽  
Vol 9 ◽  
Author(s):  
D. Graziosi ◽  
O. Nakagami ◽  
S. Kuma ◽  
A. Zaghetto ◽  
T. Suzuki ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Cultural Heritage ◽  
Motion Picture ◽  
Point Cloud ◽  
Autonomous Driving ◽  
Data Representation ◽  
Media Content ◽  
International Standard ◽  
3D Data ◽  
Immersive Media

Abstract This article presents an overview of the recent standardization activities for point cloud compression (PCC). A point cloud is a 3D data representation used in diverse applications associated with immersive media including virtual/augmented reality, immersive telepresence, autonomous driving and cultural heritage archival. The international standard body for media compression, also known as the Motion Picture Experts Group (MPEG), is planning to release in 2020 two PCC standard specifications: video-based PCC (V-CC) and geometry-based PCC (G-PCC). V-PCC and G-PCC will be part of the ISO/IEC 23090 series on the coded representation of immersive media content. In this paper, we provide a detailed description of both codec algorithms and their coding performances. Moreover, we will also discuss certain unique aspects of point cloud compression.

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