TIN generation and point-cloud compression for vehicle-based mobile mapping systems

Masuda, Hiroshi; He, Jin

doi:10.1016/j.aei.2015.05.007

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…We compared our method with the scan-line based compression (Masuda et al, 2015). This method was designed for MMS data, and compresses scan-lines using the second-order differences.…”

Section: Experimantal Resultsmentioning

confidence: 99%

“…They aligned scan lines in a lattice manner, and compressed them using an image compression method. (Masuda et al, 2015) also proposed a scan-line based compression method, in which a sequence of points was encoded using the second-order differences. (Tu et al, 2016) compressed raw point data of the Velodyne HDL-64S2, which emits 64 laser beams simultaneously.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Point-Cloud Compression for Vehicle-Based Mobile Mapping Systems Using Portable Network Graphics

Kohira

Masuda

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 KEY WORDS: Mobile Mapping System, point-clouds, lossless compression ABSTRACT:A mobile mapping system is effective for capturing dense point-clouds of roads and roadside objects．Point-clouds of urban areas, residential areas, and arterial roads are useful for maintenance of infrastructure, map creation, and automatic driving. However, the data size of point-clouds measured in large areas is enormously large. A large storage capacity is required to store such point-clouds, and heavy loads will be taken on network if point-clouds are transferred through the network. Therefore, it is desirable to reduce data sizes of point-clouds without deterioration of quality. In this research, we propose a novel point-cloud compression method for vehicle-based mobile mapping systems. In our compression method, point-clouds are mapped onto 2D pixels using GPS time and the parameters of the laser scanner. Then, the images are encoded in the Portable Networking Graphics (PNG) format and compressed using the PNG algorithm. In our experiments, our method could efficiently compress point-clouds without deteriorating the quality.

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“…We compared our method with the scan-line based compression (Masuda et al, 2015). This method was designed for MMS data, and compresses scan-lines using the second-order differences.…”

Section: Experimantal Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Point-Cloud Compression for Vehicle-Based Mobile Mapping Systems Using Portable Network Graphics

Kohira

Masuda

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…To remedy this problem, we convert point-clouds to mesh models and fill gaps on image space by projecting triangle faces. Our mesh generation method is based on a GPS-time based triangulation method (Masuda and He, 2015).…”

Section: Converting Point-clouds Into Mesh Modelmentioning

confidence: 99%

Refinement of Colored Mobile Mapping Data Using Intensity Images

Yamakawa¹,

Fukano²,

Onodera³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Mobile mapping systems (MMS) can capture dense point-clouds of urban scenes. For visualizing realistic scenes using point-clouds, RGB colors have to be added to point-clouds. To generate colored point-clouds in a post-process, each point is projected onto camera images and a RGB color is copied to the point at the projected position. However, incorrect colors are often added to point-clouds because of the misalignment of laser scanners, the calibration errors of cameras and laser scanners, or the failure of GPS acquisition. In this paper, we propose a new method to correct RGB colors of point-clouds captured by a MMS. In our method, RGB colors of a point-cloud are corrected by comparing intensity images and RGB images. However, since a MMS outputs sparse and anisotropic point-clouds, regular images cannot be obtained from intensities of points. Therefore, we convert a point-cloud into a mesh model and project triangle faces onto image space, on which regular lattices are defined. Then we extract edge features from intensity images and RGB images, and detect their correspondences. In our experiments, our method worked very well for correcting RGB colors of pointclouds captured by a MMS.

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“…Over the past decade, with the development of laser scanning, photographic measurement, and unmanned aerial vehicles, archiving of large-scale 3D objects became possible [7]. A vehicle-based mobile mapping system that integrates a camera, laser scanner, inertial measurement unit, and global positioning system provides an efficient way to generate a 3D point cloud [8]. Precise measurement of large-scale 3D objects has advanced digital archiving of large-scale cultural heritage assets [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Fused Transparent Visualization of Point Cloud Data and Background Photographic Image for Tangible Cultural Heritage Assets

Li¹,

Hasegawa²,

Nii³

et al. 2019

IJGI

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Digital archiving of three-dimensional cultural heritage assets has increased the demand for visualization of large-scale point clouds of cultural heritage assets acquired by laser scanning. We proposed a fused transparent visualization method that visualizes a point cloud of a cultural heritage asset in an environment using a photographic image as the background. We also proposed lightness adjustment and color enhancement methods to deal with the reduced visibility caused by the fused visualization. We applied the proposed method to a laser-scanned point cloud of a high-valued cultural festival float with complex inner and outer structures. Experimental results demonstrate that the proposed method enables high-quality transparent visualization of the cultural asset in its surrounding environment.

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TIN generation and point-cloud compression for vehicle-based mobile mapping systems

Cited by 13 publications

References 13 publications

Point-Cloud Compression for Vehicle-Based Mobile Mapping Systems Using Portable Network Graphics

Point-Cloud Compression for Vehicle-Based Mobile Mapping Systems Using Portable Network Graphics

Refinement of Colored Mobile Mapping Data Using Intensity Images

Fused Transparent Visualization of Point Cloud Data and Background Photographic Image for Tangible Cultural Heritage Assets

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