Point Data Reduction Using 3D Grids

Lee, K. H.; Woo, Hee-Chul; Suk, Tomáš

doi:10.1007/s001700170075

Cited by 77 publications

(47 citation statements)

References 10 publications

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“…In this research, a 3D Grid algorithm [27] is adopted to reduce the complexity of the geometric model. It subdivides the point data set into a number of sub-grids according to the prescribed geometric and color error, and each sub-grid is represented by one point.…”

Section: Efficient Sampling For Surface Splattingmentioning

confidence: 99%

Efficient reduction of point data sets for surface splatting using geometry and color attributes

Kim

Pajarola

Lee

2011

Int J Adv Manuf Technol

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Surface splat, one of the point-based rendering primitives, has offered a powerful alternative to triangle meshes when it comes to the rendering of highly complex objects due to its potential for highperformance and high-quality rendering. Recently, the technological advance of 3D scanners has made it possible to acquire color as well as geometry data of highly complex objects with very high speed and accuracy. However, scanning and acquisition systems often produce surfaces that are much more dense than actually required for the intended application. Therefore, reduction of point data set is necessary to further process the model. Although many efficient sampling methods for point-based surfaces have been proposed to reduce the complexity of geometric models, none of these has taken into account color, which is fundamental for achieving a high quality visual appearance. Therefore, we propose an efficient sampling method of point data sets for surface splatting which uses both geometry and color attributes. Our proposed method converts a dense set of point samples into a sparse set of object space splats. It successfully approximates of the original model within a given geometric and color error. In order to measure color differences between point samples with consistency, the color error tolerance is evaluated in a CIELAB uniform color space. Abstract Surface splat, one of the point based rendering primitives, has offered a powerful alternative to triangle meshes when it comes to the rendering of highly complex objects due to its potential for high-performance and high-quality rendering. Recently the technological advance of 3D scanners has made it possible to acquire color as well as geometry data of highly complex objects with very high speed and accuracy. However, scanning and acquisition systems often produce surfaces that are much more dense than actually required for the intended application. Therefore, reduction of point data set is necessary to further process the model. Although many efficient sampling methods for point-based surfaces have been proposed to reduce the complexity of geometric models, none of these have taken into account color, which is fundamental for achieving a high quality visual appearance. Therefore, we propose an efficient sampling method of point data sets for surface splatting which uses both geometry and color attributes. Our proposed method converts a dense set of point samples into a sparse set of object space splats. It successfully approximates of the original model within a given geometric and color error. In order to measure color differences between point samples with consistency, the color error tolerance is evaluated in a CIELAB uniform color space.

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Section: Efficient Sampling For Surface Splattingmentioning

confidence: 99%

Efficient reduction of point data sets for surface splatting using geometry and color attributes

Kim

Pajarola

Lee

2011

Int J Adv Manuf Technol

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“…The latter surpassed 2D in the judgment of deepness and improves the credibility in data simplification. Aiming at Color 3D data processing [7]^ this thesis takes color boundary restriction into consideration and avoids the color distortion after model reconstruction.…”

Section: Data Preprocessmentioning

confidence: 99%

Data of scanned points' pre-process and curved surface's reconstruction /

Wang¹

2009

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“…This category may also be divided into point-based sampling and mesh-based sampling. Based on mesh, there exist different reviews such as [2] and [9]. Recently, Castello et al [1] presented a reduction polygonal meshes approach using view-based techniques to estimate regions where traditional methods will discard a higher number of polygons, improving the reconstruction results for visual significant surfaces.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of sampling method effects in 3D non-rigid registration

Saval-Calvo

Azorín-López

Fuster-Guilló

et al. 2016

Neural Comput & Applic

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Since the beginning of 3D computer vision problems, the use of techniques to reduce the data to make it treatable preserving the important aspects of the scene has been necessary. Currently, with the new low-cost RGB-D sensors, which provide a stream of color and 3D data of approximately 30 frames per second, this is getting more relevance. Many applications make use of these sensors, and need a preprocessing to downsample the data in order to either reduce the processing time or improve the data (e.g. reducing noise or enhancing the important features). In this paper, we present a comparison of different downsampling techniques which are based on different principles. Concretely, five different downsampling methods are included: a bilinear-based method, a normal-based, a color-based, a combination of the normal and colorbased samplings, and a Growing Neural Gas (GNG) based approach. For the comparison, two different models have been used acquired with the Blensor software. Moreover, to evaluate the effect of the downsampling in a real application, a 3D non-rigid registration is performed with the data sampled. From the experimentation we can conclude that depending on the purpose of the application some kernels of the sampling methods can improve drastically the results. Bilinear and GNG-based methods provide homogeneous point clouds, but color-based and normal-based provide datasets with higher density of points in areas with specific features. In the non-rigid application, This study was supported in part by the University of Alicante and Spanish government under grants GRE11-01 and DPI2013-40534-R. if a color-based sampled point cloud is used, it is possible to properly register two datasets for cases where intensity data is relevant in the model, and outperform the results if only a homogeneous sampling is used.

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Point Data Reduction Using 3D Grids

Cited by 77 publications

References 10 publications

Efficient reduction of point data sets for surface splatting using geometry and color attributes

Efficient reduction of point data sets for surface splatting using geometry and color attributes

Data of scanned points' pre-process and curved surface's reconstruction /

Evaluation of sampling method effects in 3D non-rigid registration

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