Co-registration of Surfaces by 3D Least Squares Matching

Akça, Devrim

doi:10.14358/pers.76.3.307

Cited by 61 publications

(53 citation statements)

References 11 publications

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“…Automatic co-registration of the DTMs on the reference DTM was conducted with LS3D (Least Squares 3D Matching software; Akca, 2010). This method estimates the transformation parameters of one DTM relative to the reference using the generalised Gauss-Markoff model, and minimising the sum of squares of the Euclidean distances between the surfaces (Gruen and Akca, 2005).…”

Section: Co-registration Of Dtmsmentioning

confidence: 99%

Monitoring topographic changes in a periglacial high‐mountain face using high‐resolution DTMs, Monte Rosa East Face, Italian Alps

Fischer

Eisenbeiss

Kääb

et al. 2011

Permafrost & Periglacial

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This paper describes a remote sensing-based approach for detailed topographic investigations in steep periglacial high-mountain faces. The study was conducted at one of the highest periglacial rock faces in the European Alps, the permafrost-affected and partially glacierised east face of Monte Rosa. Strongly increased rock and ice avalanche activity on this rock wall has caused major topographic change in recent decades. A time series of high-resolution digital terrain models (DTMs) with a 2-m resolution was produced from digital aerial photogrammetry for 1956, 1988 and 2001 and from airborne LiDAR for 2005 and 2007. The DTM comparisons reveal a total volume loss of permafrost-affected bedrock and glacier ice of more than 20 Â 10 6 m 3 over the past 50 years, with the majority of the loss since 1988. Analysis of all unstable areas and detachment zones showed that the sequence of the main slope failures is spatially connected and that there is coupling between permafrost bedrock instability and the condition of adjacent hanging glaciers.

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Section: Co-registration Of Dtmsmentioning

confidence: 99%

Monitoring topographic changes in a periglacial high‐mountain face using high‐resolution DTMs, Monte Rosa East Face, Italian Alps

Fischer

Eisenbeiss

Kääb

et al. 2011

Permafrost & Periglacial

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“…These scans (in the form of point clouds) are combined into a co-registered mosaic to cover the entire surface of the related epoch, using the least square 3D surface matching (LS3D) method (Gruen and Akca, 2005;Akca, 2010).…”

Section: Co-registration and Surface Generationmentioning

confidence: 99%

Laboratory Flume Experiment With a Coded Structured Light System

Akça

Seybold

2012

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

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ABSTRACT:The topography of inland deltas is influenced chiefly by the water-sediment balance in distributary channels and local evaporation and seepage rates. In a previous study, a reduced complexity model has been applied to simulate the process of inland delta formation. Results have been compared with the Okavango Delta, Botswana and with a laboratory experiment. Both in the macro scale and the micro scale cases, high quality digital elevation models (DEM) are essential. This work elaborates the laboratory experiment where an artificial inland delta is generated on laboratory scale and its topography is measured using a Breuckmann 3D scanner. The space-time evolution of the inland delta is monitored in the consecutive DEM layers. Regarding the 1.0m x 1.0m x 0.3m size of the working area, better than 100 micron precision is achieved which gives a relative precision of 1/10 000. The entire 3D modelling workflow is presented in terms of scanning, co-registration, surface generation, editing, and visualization steps. The co-registered high resolution topographic data allows us to analyse the stratigraphy patterns of the experiment and gain quantitative insight into the spatio-temporal evolution of the delta formation process.

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“…Many follow-up improvements [5][6][7][8] have promoted ICP efficiency. In addition, other related studies, such as the iterative closest patch [9], the iterative closest projected point [10], and the least-squares surface matching method (LS3D) [11,12], have also proven their effectiveness in point cloud registration. On the other hand, numerous feature-based techniques have been developed based on geometric primitives, such as points, lines [13][14][15][16], planes [13,[17][18][19][20], curves and surface [21], and specific objects [22].…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Registration of Point Clouds

Chuang

Jaw

2017

Remote Sensing

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Light detection and ranging (LiDAR) has become a mainstream technique for rapid acquisition of 3-D geometry. Current LiDAR platforms can be mainly categorized into spaceborne LiDAR system (SLS), airborne LiDAR system (ALS), mobile LiDAR system (MLS), and terrestrial LiDAR system (TLS). Point cloud registration between different scans of the same platform or different platforms is essential for establishing a complete scene description and improving geometric consistency. The discrepancies in data characteristics should be manipulated properly for precise transformation estimation. This paper proposes a multi-feature registration scheme suitable for utilizing point, line, and plane features extracted from raw point clouds to realize the registrations of scans acquired within the same LIDAR system or across the different platforms. By exploiting the full geometric strength of the features, different features are used exclusively or combined with others. The uncertainty of feature observations is also considered within the proposed method, in which the registration of multiple scans can be simultaneously achieved. The simulated test with an ideal geometry and data simplification was performed to assess the contribution of different features towards point cloud registration in a very essential fashion. On the other hand, three real cases of registration between LIDAR scans from single platform and between those acquired by different platforms were demonstrated to validate the effectiveness of the proposed method. In light of the experimental results, it was found that the proposed model with simultaneous and weighted adjustment rendered satisfactory registration results and showed that not only features inherited in the scene can be more exploited to increase the robustness and reliability for transformation estimation, but also the weak geometry of poorly overlapping scans can be better treated than utilizing only one single type of feature. The registration errors of multiple scans in all tests were all less than point interval or positional error, whichever dominating, of the LiDAR data.

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Co-registration of Surfaces by 3D Least Squares Matching

Cited by 61 publications

References 11 publications

Monitoring topographic changes in a periglacial high‐mountain face using high‐resolution DTMs, Monte Rosa East Face, Italian Alps

Monitoring topographic changes in a periglacial high‐mountain face using high‐resolution DTMs, Monte Rosa East Face, Italian Alps

Laboratory Flume Experiment With a Coded Structured Light System

Multi-Feature Registration of Point Clouds

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