Efficient and Flexible Sampling with Blue Noise Properties of Triangular Meshes

Corsini, Massimiliano; Cignoni, Paolo; Scopigno, Roberto

doi:10.1109/tvcg.2012.34

Cited by 285 publications

(160 citation statements)

References 24 publications

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“…This point cloud was inspected manually, acquisition and/or registration errors were corrected manually using MeshLab (Cignoni et al, 2008), and the cleaned point cloud was meshed to generate a set of polygons representing the surface of the plant using available open-source software (Bernardini et al, 1999;Corsini et al, 2012;Kazhdan and Hoppe, 2013). The plant mesh was then segmented into a shoot cylinder (composed of the stem and leaf sheaths), individual leaves, and an inflorescence (when present; Supplemental Fig.…”

Section: Plants Greenhouse Conditions Manual Measurements and Imagmentioning

confidence: 99%

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

2016

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Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.

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Section: Plants Greenhouse Conditions Manual Measurements and Imagmentioning

confidence: 99%

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

2016

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“…The exported model was then decimated in MeshLab 2016.12 (Cignoni et al, 2008) via poisson disk sampling (Corsini et al, 2012) and screened poisson surface reconstruction (Kazdhan et al, 2013) to yield three textured, downsampled versions for web viewing and 3D printing.…”

Section: Structured Light Scanningmentioning

confidence: 99%

Epic Dimensions: A Comparative Analysis of 3d Acquisition Methods

Graham

Akoglu

Lassen

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:When it comes to capturing the geometry of a cultural heritage artifact, there is certainly no dearth of possible acquisition techniques. As technology has rapidly developed, the availability of intuitive 3D generating tools has increased exponentially and made it possible even for non-specialists to create many models quickly. Though the by-products of these different acquisition methods may be incongruent in terms of quality, these discrepancies are not problematic, as there are many applications of 3D models, each with their own set of requirements. Comparisons of high-resolution 3D models of an iconic Babylonian tablet, captured via four different closerange technologies discussed in this paper assess which methods of 3D digitization best suit specific intended purposes related to research, conservation and education. Taking into consideration repeatability, time and resource implications, qualitative and quantitative potential and ease of use, this paper presents a study of the strengths and weakness of structured light scanning, triangulation laser scanning, photometric stereo and close-range photogrammetry, in the context of interactive investigation, conditions monitoring, engagement, and dissemination.

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“…For the reconstruction quality of P, like the completeness, we use the approximated surface coverage cov (in %) between a reference point cloud Pref and P. Both point clouds are subsampled via Poisson Disk Sampling (Corsini et al, 2012) to get equivalent point distances, followed by spherical region growing (Section 3.3.1) for each point and then counting the sphere intersections between both point clouds. For detecting and measuring gaps in the reconstructed point cloud, the averaged absolute "mesh-to-point" distancedabs, between a meshed reference surface and P is calculated.…”

Section: Evaluation Strategymentioning

confidence: 99%

Next-Best-View Method Based on Consecutive Evaluation of Topological Relations

Dierenbach

Weinmann

Jutzi

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This work describes an iterative algorithm for estimating optimal viewpoints, so called next-best-views (NBVs). The goal is to incrementally construct a topological network from the scene during the consecutive acquisition of several views. Our approach is a hybrid method between a surface-based and a volumetric approach with a continuous model space. Hence, a new scan taken from an optimal position should either cover as much as possible from the unknown object surface in one single scan, or densify the existing data and close possible gaps. Based on the point density, we recover the essential and structural information of a scene based on the Growing Neural Gas (GNG) algorithm. From the created graph representation of topological relations, the density of the point cloud at each network node is estimated by approximating the volume of Voronoi cells. The NBV Finder selects a network node as NBV, which has the lowest point density. Our NBV method is self-terminating when all regions reach a predefined minimum point density or the change of the GNG error is zero. For evaluation, we use a Buddha statue with a rather simple surface geometry but still some concave parts and the Stanford Dragon with a more complex object surface containing occluded and concave parts. We demonstrate that our NBV method outperforms a "naive random" approach relying on uniformly distributed sensor positions in terms of efficiency, i.e. our proposed method reaches a desired minimum point density up to 20% faster with less scans.

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Efficient and Flexible Sampling with Blue Noise Properties of Triangular Meshes

Cited by 285 publications

References 24 publications

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

Epic Dimensions: A Comparative Analysis of 3d Acquisition Methods

Next-Best-View Method Based on Consecutive Evaluation of Topological Relations

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