Semantic Segmentation of 3D Point Cloud to Virtually Manipulate Real Living Space

Ishikawa, Yuki; Hachiuma, Ryo; Ienaga, Naoto; Kuno, Wakaba; Sugiura, Yuta; Saitō, Hideo

doi:10.1109/apmar.2019.8709156

Cited by 12 publications

(14 citation statements)

References 6 publications

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“…With the emergence of new technologies such as deep learning and convolutional neural networks in recent years, point cloud semantic segmentation has enjoyed a continuous development [5]- [8]. Although deep learning has been widely used in the processing of two-dimensional images, it is still difficult to perform convolution operations on irregular and disordered 3D point clouds directly.…”

Section: A Challenges Of Point Cloud Semantic Segmentationmentioning

confidence: 99%

A Review of Deep Learning-Based Semantic Segmentation for Point Cloud

et al. 2019

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In recent years, the popularity of depth sensors and 3D scanners has led to a rapid development of 3D point clouds. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. Recent advances in this topic are dominantly led by deep learning-based methods. In this paper, we provide a survey covering various aspects ranging from indirect segmentation to direct segmentation. Firstly, we review methods of indirect segmentation based on multi-views and voxel grids, as well as direct segmentation methods from different perspectives including point ordering, multi-scale, feature fusion and fusion of graph convolutional neural network (GCNN). Then, the common datasets for point cloud segmentation are exposed to help researchers choose which one is the most suitable for their tasks. Following that, we devote a part of the paper to analyze the quantitative results of these methods. Finally, the development trend of point cloud semantic segmentation technology is prospected.

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Section: A Challenges Of Point Cloud Semantic Segmentationmentioning

confidence: 99%

A Review of Deep Learning-Based Semantic Segmentation for Point Cloud

et al. 2019

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“…For rest of the points, we apply downsampling to eliminate noise. Next, we employ the work of [Ishikawa et al 2019] for class probability refinement. Finally, we perform Euclidean clustering to extract individual objects from each category.…”

Section: Individual Object Extractionmentioning

confidence: 99%

Indoor Scene Semantic Modeling for Virtual Reality

Lin

et al. 2020

SIGGRAPH Asia 2020 Posters

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“…Coloured point cloud (CPC) is one of the important representations of three-dimensional (3D) objects, it can provide more realistic visual information than ordinary images and give users better immersive experience [1]. CPC is a set of points in 3D space, which contain not only the geometry coordinate but also colour attribute.…”

Section: Introductionmentioning

confidence: 99%

CPC‐GSCT: Visual quality assessment for coloured point cloud based on geometric segmentation and colour transformation

Lee

et al. 2021

IET Image Processing

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Coloured point cloud (CPC) is one of the important representations of three-dimensional objects, which has been used in many fields. CPC may encounter geometric and colour distortion during its compression, simplification or other processing. Thus, the objective visual quality assessment of CPC is one of the urgent issues to be resolved in the CPC's applications. Aiming at this problem, this paper proposes a new full-reference visual quality assessment metric for CPC based on geometric segmentation and colour transformation (CPC-GSCT), which analyzes geometric distortion and colour distortion of CPC. First, considering the visual masking effect of CPC's geometric information, CPC is segmented into different regions and distributed with different weights to describe the influence of visual masking effect in CPC quality assessment. At the same time, a geometric combination feature vector is defined and extracted for measuring the CPC's geometric distortion. Then, considering the colour perception of human eyes, a colour combination feature vector is extracted to measure the CPC's colour distortion in HSV colour space. Finally, all the extracted geometric and colour features are constituted as a feature vector to predict the quality of CPC. Experimental results on three databases (IRPC, SJTU-PCQA and CPCD2.0) show that the proposed CPC-GSCT metric can achieve better performance in predicting the visual quality of CPC than relevant existing methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Semantic Segmentation of 3D Point Cloud to Virtually Manipulate Real Living Space

Cited by 12 publications

References 6 publications

A Review of Deep Learning-Based Semantic Segmentation for Point Cloud

A Review of Deep Learning-Based Semantic Segmentation for Point Cloud

Indoor Scene Semantic Modeling for Virtual Reality

CPC‐GSCT: Visual quality assessment for coloured point cloud based on geometric segmentation and colour transformation

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