Deep Learning on Point Clouds and Its Application: A Survey

Liu, Weiping; Sun, Jia; Li, Wanyi; Hu, Ting; Wang, Peng

doi:10.3390/s19194188

Cited by 180 publications

(123 citation statements)

References 94 publications

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“…In general, machine learning and its subset deep learning solutions have seen a surge in popularity in these recent years since the advent of big data [32]. Machine learning approaches are robust against noise and occlusions, and generally reliable.…”

Section: Machine Learning and Deep Learning Approachesmentioning

confidence: 99%

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Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

Murtiyoso

Grussenmeyer

2020

Sensors

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3D heritage documentation has seen a surge in the past decade due to developments in reality-based 3D recording techniques. Several methods such as photogrammetry and laser scanning are becoming ubiquitous amongst architects, archaeologists, surveyors, and conservators. The main result of these methods is a 3D representation of the object in the form of point clouds. However, a solely geometric point cloud is often insufficient for further analysis, monitoring, and model predicting of the heritage object. The semantic annotation of point clouds remains an interesting research topic since traditionally it requires manual labeling and therefore a lot of time and resources. This paper proposes an automated pipeline to segment and classify multi-scalar point clouds in the case of heritage object. This is done in order to perform multi-level segmentation from the scale of a historical neighborhood up until that of architectural elements, specifically pillars and beams. The proposed workflow involves an algorithmic approach in the form of a toolbox which includes various functions covering the semantic segmentation of large point clouds into smaller, more manageable and semantically labeled clusters. The first part of the workflow will explain the segmentation and semantic labeling of heritage complexes into individual buildings, while a second part will discuss the use of the same toolbox to segment the resulting buildings further into architectural elements. The toolbox was tested on several historical buildings and showed promising results. The ultimate intention of the project is to help the manual point cloud labeling, especially when confronted with the large training data requirements of machine learning-based algorithms.

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Section: Machine Learning and Deep Learning Approachesmentioning

confidence: 99%

“…Various types of machine learning and deep learning techniques are available, as described in [32]. In [35], a comparison on several machine learning and deep learning techniques were performed.…”

Section: Machine Learning and Deep Learning Approachesmentioning

confidence: 99%

Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

Murtiyoso

Grussenmeyer

2020

Sensors

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“…As one of the photogrammetric products, the point clouds generated from multi-view stereo/ stereo or LiDAR scanning are sometimes considered as raw input for many applications such as classification and change detection (Hebel et al, 2013;Liu et al, 2019b;Teo and Shih, 2013). Although there have been many approaches for point clouds classification, obtaining high-quality classified point clouds in practice (e.g.…”

Section: Semantic Interpretationmentioning

confidence: 99%

The role of machine intelligence in photogrammetric 3D modeling – an overview and perspectives

Qin

Gruen

2020

International Journal of Digital Earth

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The process of modern photogrammetry converts images and/or LiDAR data into usable 2D/3D/4D products. The photogrammetric industry offers engineering-grade hardware and software components for various applications. While some components of the data processing pipeline work already automatically, there is still substantial manual involvement required in order to obtain reliable and high-quality results. The recent development of machine learning techniques has attracted a great attention in its potential to address complex tasks that traditionally require manual inputs. It is therefore worth revisiting the role and existing efforts of machine learning techniques in the field of photogrammetry, as well as its neighboring field computer vision. This paper provides an overview of the state-of-the-art efforts in machine learning in bringing the automated and 'intelligent' component to photogrammetry, computer vision and (to a lesser degree) to remote sensing. We will primarily cover the relevant efforts following a typical 3D photogrammetric processing pipeline: (1) data acquisition (2) georeferencing/interest point matching (3) Digital Surface Model generation (4) semantic interpretations, followed by conclusions and our insights.

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“…In our implementation we have used unoptimized NN algorithm, where we compare input vector to each other vector in the dataset to find the nearest neighbor. We are aware of the existence of searching algorithms that accelerates computation like kd-trees [56] Semi-Convex Hull Tree [57] or Dynamic Continuous Indexin [58]; however, they will not improve the recognition rate of the approach, which was a goal of our research.…”

Section: Classificationmentioning

confidence: 99%

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

Hachaj

Piekarczyk

2019

Sensors

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The motivation of this paper is to examine the effectiveness of state-of-the-art and newly proposed motion capture pattern recognition methods in the task of head gesture classifications. The head gestures are designed for a user interface that utilizes a virtual reality helmet equipped with an internal measurement unit (IMU) sensor that has 6-axis accelerometer and gyroscope. We will validate a classifier that uses Principal Components Analysis (PCA)-based features with various numbers of dimensions, a two-stage PCA-based method, a feedforward artificial neural network, and random forest. Moreover, we will also propose a Dynamic Time Warping (DTW) classifier trained with extension of DTW Barycenter Averaging (DBA) algorithm that utilizes quaternion averaging and a bagged variation of previous method (DTWb) that utilizes many DTW classifiers that perform voting. The evaluation has been performed on 975 head gesture recordings in seven classes acquired from 12 persons. The highest value of recognition rate in a leave-one-out test has been obtained for DTWb and it equals 0.975 (0.026 better than the best of state-of-the-art methods to which we have compared our approach). Among the most important applications of the proposed method is improving life quality for people who are disabled below the neck by supporting, for example, an assistive autonomous power chair with a head gesture interface or remote controlled interfaces in robotics.

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Deep Learning on Point Clouds and Its Application: A Survey

Cited by 180 publications

References 94 publications

Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

The role of machine intelligence in photogrammetric 3D modeling – an overview and perspectives

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

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