3D point cloud compression using conventional image compression for efficient data transmission

Houshiar, Hamidreza; Nüchter, Andreas

doi:10.1109/icat.2015.7340499

Cited by 67 publications

(43 citation statements)

References 19 publications

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“…It is shown that the reduced point clouds are ideally suited for feature-based registration on panorama images. In [24] the same authors propose the use of conventional image-based compression methods for 3D point clouds. The point cloud is mapped onto panorama images using equirectangular projection, to encode the range, reflectance and color value for each point.…”

Section: Methods Based On Projectionsmentioning

confidence: 99%

Study of Subjective and Objective Quality Evaluation of 3D Point Cloud Data by the JPEG Committee

Perry¹,

Pinheiro²,

Dumic³

et al. 2019

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The SC29/WG1 (JPEG) Committee within ISO/IEC is currently working on developing standards for the storage, compression and transmission of 3D point cloud information. To support the creation of these standards, the committee has created a database of 3D point clouds representing various quality levels and use-cases and examined a range of 2D and 3D objective quality measures. The examined quality measures are correlated with subjective judgments for a number of compression levels. In this paper we describe the database created, tests performed and key observations on the problems of 3D point cloud quality assessment.

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Section: Methods Based On Projectionsmentioning

confidence: 99%

Study of Subjective and Objective Quality Evaluation of 3D Point Cloud Data by the JPEG Committee

Perry¹,

Pinheiro²,

Dumic³

et al. 2019

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show abstract

“…Some studies have tried converting 3D point cloud data into a 2D structure rather than decomposing one frame of point cloud data into multiple images. Houshiar and Nüchter used an equirectangular projection to map point clouds onto panorama images [30]. Kohira and Masuda mapped point cloud data onto 2D pixels using GPS time and the parameters of the laser scanner [31].…”

Section: Related Workmentioning

confidence: 99%

Prioritized Transmission Control of Point Cloud Data Obtained by LIDAR Devices

et al. 2020

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Smart monitoring, particularly at intersections, is a promising service that is being considered for the concept of smart cities. A network of light detection and ranging (LIDAR) sensors, which generates point cloud data in real time, can be used to detect people's mobility in smart monitoring. Due to the sheer volume of point cloud data, data transmission requires a significant amount of communication resources. In order to monitor people's mobility in real time, it is necessary to reduce the amount of transmission data to shorten delay. Point cloud compression is one method for reducing the amount of data. However, prior works addressing point cloud compression mainly focused on accuracy for the compression of an entire point cloud without considering its spatial characteristics. The more dynamically a spatial region changes, the more important it is when detecting moving objects such as cars, trucks, pedestrians, and bikes in smart monitoring. This paper proposes a prioritized transmission scheme that applies multiple point cloud compression methods to point cloud data according to the spatial importance of the data, i.e., how dynamically spatial regions change. This paper assumes data transmission of point cloud data from multiple LIDAR devices to an edge server and addresses the intra-frame geometry compression of point cloud data. The proposed scheme splits the point cloud into multiple classes according to the spatial importance and applies multiple point cloud compression methods to each class. A numerical study using a real point cloud dataset obtained at an intersection demonstrates the dependencies of quality, volume, and processing time on possible compression format options. The results verify that the proposed scheme reduces the amount of point cloud data drastically while satisfying the quality and processing time requirements.

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“…Furthermore, imagery can be processed into 3D point clouds using Structure from Motion (SfM) by estimating camera parameters and 3D structural information of scenes (Snavely et al., ). Vice versa, 3D point clouds can also be converted to 2D images, for example, for lossless data compression (Houshiar and Nüchter, ) and as‐built surface patches (Henry et al., ).…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Generation of Semantically Rich As‐Built Building Information Models Using 2D Images: A Derivative‐Free Optimization Approach

Xue

Chen

2018

Computer aided Civil Eng

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Over the past decade a considerable number of studies have focused on generating semantically rich as-built building information models (BIMs). However, the prevailing methods rely on laborious manual segmentation or automatic but error-prone segmentation. In addition, the methods failed to make good use of existing semantics sources. This article presents a novel segmentation-free derivative-free optimization (DFO) approach that translates the generation of asbuilt BIMs from 2D images into an optimization problem of fitting BIM components regarding architectural and topological constraints. The semantics of the BIMs are subsequently enriched by linking the fitted components with existing semantics sources. The approach was prototyped in two experiments using an outdoor and an indoor case, respectively. The results showed that in the outdoor case 12 out of 13 BIM components were correctly generated within 1.5 hours, and in the indoor case all target BIM components were correctly generated with a root-mean-square deviation (RMSD) of 3.9 cm in about 2.5 hours. The main computational novelties of this study are: (1) to translate the automatic as-built BIM generation from 2D images as an optimization problem; (2) to develop an effective and segmentation-free approach that is fundamentally different from prevailing methods; and (3) to exploit online open BIM component information for semantic enrichment, which, to a certain extent, alleviates the dilemma between information inadequacy and information overload in BIM development.

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3D point cloud compression using conventional image compression for efficient data transmission

Cited by 67 publications

References 19 publications

Study of Subjective and Objective Quality Evaluation of 3D Point Cloud Data by the JPEG Committee

Study of Subjective and Objective Quality Evaluation of 3D Point Cloud Data by the JPEG Committee

Prioritized Transmission Control of Point Cloud Data Obtained by LIDAR Devices

Automatic Generation of Semantically Rich As‐Built Building Information Models Using 2D Images: A Derivative‐Free Optimization Approach

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