From point-based to feature-based aerial triangulation

Schenk, Toni

doi:10.1016/j.isprsjprs.2004.02.003

Cited by 64 publications

(45 citation statements)

References 4 publications

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“…Methods can also be differentiated based on which image features they use for matching. Some authors propose points (Ding & Zakhor 2008, Avbelj et al 2010), but most works consider lines as more natural for building structures and use them for co-registration (Debevec 1996, Früh et al 2004, Schenk 2004, Eugster & Nebiker 2009). In some papers hybrid methods employing points and lines at the same time are presented (Zhang et al 2005, Tian et al 2008).…”

Section: Related Workmentioning

confidence: 99%

“…On the one hand linear structures are more suitable than points to represent building, on the other hand they are more difficult to represent, especially in 3D using e.g. parameterization with two angles and two coordinates (Schenk, 2004 or Plücker coordinates. Such representation leads to more complicated projection matrix than projection matrix for points.…”

Section: Model-to-image Matchingmentioning

confidence: 99%

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Model-to-Image Registration and Automatic Texture Mapping Using a Video Sequence Taken by a Mini Uav

Iwaszczuk¹,

Helmholz²,

Belton³

et al. 2013

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

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ABSTRACT:3D city models are used in many fields. Photorealistic building textures find applications such as façade reconstruction, thermal building inspections and heat leakage detection using thermal infrared (TIR) images, quantitative evaluation or study of the materials lying on the object's surface using multispectral images. Often texturing cannot be done using the same data which was used for 3D reconstruction or textures have to be updated. In such cases co-registration between 3D building models and images has to be carried out. In this paper we present a method for model-to-image matching and texture extraction with best texture selection procedure. We present results for two data sets, first for TIR image sequences taken from a helicopter and second for VIS images taken from an Unmanned Aerial Vehicle (UAV).

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Section: Related Workmentioning

confidence: 99%

Section: Model-to-image Matchingmentioning

confidence: 99%

Model-to-Image Registration and Automatic Texture Mapping Using a Video Sequence Taken by a Mini Uav

Iwaszczuk¹,

Helmholz²,

Belton³

et al. 2013

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

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“…Since a 3D line only has 4 degrees of freedom, we can search for a proper representation to deal with uncertain transformations. Minimal representations used in (Roberts, 1988), (Schenk, 2004) and (Bartoli and Sturm, 2005) inspired the representation proposed in 3.2.…”

Section: A State Of the Artmentioning

confidence: 99%

Probabilistic Feature Matching Applied to Primitive Based Registration of TLS Data

Hullo

Thibault²,

Grussenmeyer

et al. 2012

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

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ABSTRACT:Many industrial applications require dense point clouds of the installations. Acquisition of the rooms, filled with many objects, of an industrial scene leads to many Terrestrial Laser Scanner (TLS) stations. A precise registration of all the per-station point clouds is crucial for the required accuracy of 1-2 cm of final data. Targets and tachometry, current best practice for registration, slows down the survey and limits the number of campaigns. Indoor geolocation system are faster but do not reach the final required accuracy. Otherwise, 3D primitives can be automatically extracted from the dense point clouds and possibly used for registration. In a four step primitive-based registration, Acquisition -Reconstruction -Matching -Solving, the matching is crucial. This article presents a probabilistic test for 3D lines matching using a priori distributions of approximated transformations. The stochastic model of approximated transformations and resulting uncertain lines is introduced. A test is performed on a real dataset of an industrial scene and the results are analysed. Improvements of the presented test and matching framework are also discussed.

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“…Some examples can be found in Malmström (1986), Tommaselli and Tozzi (1996), Heipke (1997), Schenk (2004), Fraser et al (2001), Marcato Junior and, among others.…”

Section: Introductionmentioning

confidence: 98%

Multi-Scale Matching for the Automatic Location of Control Points in Large Scale Aerial Images Using Terrestrial Scenes

Berveglieri

Tommaselli

2014

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

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ABSTRACT:A technique to automatically locate Ground Control Points (GCPs) in large aerial images is presented considering the availability of low accuracy direct georeferencing data. The approach is based on image chips of GCPs extracted from vertical terrestrial images. A strategy combining image matching techniques was implemented to select correct matches. These matches were used to define a 2D transformation with which the GCP is projected close to its correct position, reducing the search space in the aerial image. Area-based matching with some refinements is used to locate GCPs with sub-pixel precision. Experiments were performed with multi-scale images and assessed with a bundle block adjustment simulating an indirect sensor orientation. The accuracy analysis was accomplished based on discrepancies obtained from GCPs and check points. The results were better than interactive measurements and a planimetric accuracy of 1/5 of the Ground Sample Distance (GSD) for the check points was achieved.

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From point-based to feature-based aerial triangulation

Cited by 64 publications

References 4 publications

Model-to-Image Registration and Automatic Texture Mapping Using a Video Sequence Taken by a Mini Uav

Model-to-Image Registration and Automatic Texture Mapping Using a Video Sequence Taken by a Mini Uav

Probabilistic Feature Matching Applied to Primitive Based Registration of TLS Data

Multi-Scale Matching for the Automatic Location of Control Points in Large Scale Aerial Images Using Terrestrial Scenes

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