3D Point Cloud Matching Technology Based on Depth Image Based Rendering

2021 ◽  
Author(s):  
Guo-Han Lin ◽  
You-Sheng Xiao ◽  
Hao-An Hsieh ◽  
Kuan-Yu Liao ◽  
Yi-Cheng Liu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Depth Image ◽  
3D Point Cloud ◽  
Image Based Rendering ◽  
Depth Image Based Rendering

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.

A BACKGROUND MODELING-BASED FAITHFUL APPROACH TO THE DISOCCLUSION PROBLEM IN DEPTH IMAGE-BASED RENDERING

2013 ◽  
Vol 27 (02) ◽  
pp. 1354005 ◽  
Author(s):  
MICHAEL SCHMEING ◽  
XIAOYI JIANG
Keyword(s):  
Background Modeling ◽  
View Synthesis ◽  
Depth Image ◽  
Image Based Rendering ◽  
Texture Information ◽  
Depth Image Based Rendering ◽  
Compression Artifacts

In this paper, we address the disocclusion problem that occurs during view synthesis in depth image-based rendering (DIBR). We propose a method that can recover faithful texture information for disoccluded areas. In contrast to common disocclusion filling methods, which usually work frame-by-frame, our algorithm can take information from temporally neighboring frames into account. This way, we are able to reconstruct a faithful filling for the disocclusion regions and not just an approximate or plausible one. Our method avoids artifacts that occur with common approaches and can additionally reduce compression artifacts at object boundaries.

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