Calibration and performance evaluation of a 3-D imaging sensor based on the projection of structured light

Sansoni, Giovanna; Carocci, Matteo; Rodella, R.

doi:10.1109/19.850406

Cited by 111 publications

(48 citation statements)

References 13 publications

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“…New concepts and technology including Solid State LiDAR and simultaneous localization and mapping (SLAM) have pushed dynamic acquisition for quickly mapping with a lower accuracy the surroundings, extending cases using HMLS (Hand-held mobile laser scanning) [31], MMS (Mobile Mapping System) [32], or more recently MMBS (Mobile Mapping Backpack System) [33]. At the object scale, active sensors namely for active triangulation, structured light and computer tomography for 3D modelling is widely used due to its high precision, and adaptation to small isolated objects [34]. Moving to ground technologies, surveys are precise in detecting sub-surface remains.…”

Section: Archaeological Field Workmentioning

confidence: 99%

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

Poux

Neuville

Wersch³

et al. 2017

Geosciences

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Digital investigations of the real world through point clouds and derivatives are changing how curators, cultural heritage researchers and archaeologists work and collaborate. To progressively aggregate expertise and enhance the working proficiency of all professionals, virtual reconstructions demand adapted tools to facilitate knowledge dissemination. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. In this paper, we review the state of the art of point cloud integration within archaeological applications, giving an overview of 3D technologies for heritage, digital exploitation and case studies showing the assimilation status within 3D GIS. Identified issues and new perspectives are addressed through a knowledge-based point cloud processing framework for multi-sensory data, and illustrated on mosaics and quasi-planar objects. A new acquisition, pre-processing, segmentation and ontology-based classification method on hybrid point clouds from both terrestrial laser scanning and dense image matching is proposed to enable reasoning for information extraction. Experiments in detection and semantic enrichment show promising results of 94% correct semantization. Then, we integrate the metadata in an archaeological smart point cloud data structure allowing spatio-semantic queries related to CIDOC-CRM. Finally, a WebGL prototype is presented that leads to efficient communication between actors by proposing optimal 3D data visualizations as a basis on which interaction can grow.

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Section: Archaeological Field Workmentioning

confidence: 99%

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

Poux

Neuville

Wersch³

et al. 2017

Geosciences

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“…In this method, the 3-D reconstruction procedure uses corresponding points between the left and right images [2], which are extracted from epipolar lines [3]. Since the procedure is just derived from two images and calibrated parameters, the 3-D errors are consisting of two parts: (1) the phase matching errors of corresponding points, and (2) the uncertainty of calibration [4]. Because the errors result from calibration are related to certain calibration process and tend to be predictable, they will not be discussed in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Several sinusoidal patterns with shifted phases are used for projection onto target objects and then the deformed sinusoidal patterns are captured through two cameras from different directions thus a pixel-to-pixel phase matching is acquired and 3-D points are reconstructed through triangulation [4]. As the projection device is involved, the accuracy is contributed by many projection factors such as ambient light noise, object reflectivity and curve surface [5].…”

Section: Introductionmentioning

confidence: 99%

3-D Error Estimation Based on Binocular Stereo Vision Using Fringe Projection

Wei¹,

Liu²,

Zhao³

2017

dtetr

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Abstract. The accuracy of optical 3-D shape measuring system using fringe projection is associated with many factors and usually derived from calibrated reference objects. But such kind of result is a systematic assessment which is useless when considering typical complex surfaces where measurement errors tend to be different due to variances of measuring distance, object reflectivity and curve surface. In this paper, we present a method based on phase error and binocular stereo vision in order to estimate 3-D measurement error. The relationship between 3-D measurement error and system parameters is described at first according to classical binocular stereo vision and photogrammetric principles. An algorithm of phase error calculation is proposed which is focused on captured wavelength and the 3-D measurement error is then generated. We present results derived from practical experiments which demonstrate the acceptance of this method.

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“…Techniques using Moiré gratings and sinusoids [3,4,5,6] are much more resistant to noise and reflectance variations, but requires both knowledge of the topology and points of known depth in the scene. In systems using black and white stripes [7] or colored stripes [8,9] the resolution of the range map obtained from a single image is limited by the discrete number of possible pattern encodings. Higher resolution is attained by using a temporal sequence of images and patterns.…”

Section: Introductionmentioning

confidence: 99%

High-resolution three-dimensional sensing of fast deforming objects

Fong

Buron

2005

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems

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-In applications like motion capture, high speed collision testing and robotic manipulation of deformable objects there is a critical need for capturing the 3D geometry of fast moving and/or deforming objects. Although there exist many 3D sensing techniques, most cannot deal with dynamic scenes (e.g., laser scanning). Others, like stereovision, require that object surfaces be appropriately textured. Few, if any, build highresolution 3D models of dynamic scenes. This paper presents a technique to compute high-resolution range maps from single images of moving and deforming objects. This method is based on observing the deformation of a projected light pattern that combines a set of parallel colored stripes and a perpendicular set of sinusoidal intensity stripes. While the colored stripes allow the sensor to compute absolute depths at coarse resolution, the sinusoidal intensity stripes give dense relative depths. This twofold pattern makes it possible to extract a high-resolution range map from each image in a video sequence. The sensor has been implemented and tested on several deforming objects.

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Calibration and performance evaluation of a 3-D imaging sensor based on the projection of structured light

Cited by 111 publications

References 13 publications

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

3-D Error Estimation Based on Binocular Stereo Vision Using Fringe Projection

High-resolution three-dimensional sensing of fast deforming objects

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