Deep Neural Network for 3D Point Cloud Completion with Multistage Loss Function

2019 ◽  
Author(s):  
Haohao Huang ◽  
Hongliang Chen ◽  
Jianxun Li
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
Point Cloud ◽  
Deep Neural Network ◽  
3D Point Cloud

CorsNet: 3D Point Cloud Registration by Deep Neural Network

2020 ◽  
Vol 5 (3) ◽  
pp. 3960-3966
Author(s):  
Akiyoshi Kurobe ◽  
Yusuke Sekikawa ◽  
Kohta Ishikawa ◽  
Hideo Saito
Keyword(s):  
Neural Network ◽  
Point Cloud ◽  
Deep Neural Network ◽  
3D Point Cloud ◽  
Point Cloud Registration

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.

scholarly journals Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map

Nutrients ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 2005 ◽  
Author(s):  
Frank Lo ◽  
Yingnan Sun ◽  
Jianing Qiu ◽  
Benny Lo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Point Cloud ◽  
Volume Estimation ◽  
Assessment System ◽  
View Synthesis ◽  
Depth Image ◽  
3D Point Cloud ◽  
Viewing Angle ◽  
3D Point Clouds

An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume is required. For volume estimation, previous research studies mostly focused on using model-based or stereo-based approaches which rely on manual intervention or require users to capture multiple frames from different viewing angles which can be tedious. In this paper, a view synthesis approach based on deep learning is proposed to reconstruct 3D point clouds of food items and estimate the volume from a single depth image. A distinct neural network is designed to use a depth image from one viewing angle to predict another depth image captured from the corresponding opposite viewing angle. The whole 3D point cloud map is then reconstructed by fusing the initial data points with the synthesized points of the object items through the proposed point cloud completion and Iterative Closest Point (ICP) algorithms. Furthermore, a database with depth images of food object items captured from different viewing angles is constructed with image rendering and used to validate the proposed neural network. The methodology is then evaluated by comparing the volume estimated by the synthesized 3D point cloud with the ground truth volume of the object items.

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