Alexandra Bac

Boundary extraction and simplification of a surface defined by a sparse 3D volume

Reconstructing surfaces with data coming from an automatic acquisition technique always entails the problem of mass of data. It leads to a mandatory data reduction process. Applying the process to the whole set of points induces an important risk of surface shrinking so that the initial boundary extraction is an important step permitting a simplification inside it. The global surface shape will then be better kept. It is nevertheless required to simplify the boundary, which can be done on the extracted boundary. In this paper, we present a new method to extract and simplify the boundary of an elevation surface given as voxels in a large 3D volume having the characteristics to be sparse since many data are missing. We first present our boundary definition based on mathematical relations between a point and its square neighborhoods. Second, we introduce algorithms to extract such a boundary. Third, we simplify this boundary.

Extraction of tubular shapes from dense point clouds and application to tree reconstruction from laser scanned data

Abstract We propose a novel method for detecting and reconstructing tubular shapes in dense, noisy, occluded and unorganized point clouds. The STEP method (Snakes for Tuboid Extraction from Point clouds) was originally designed to reconstruct woody parts of trees scanned with terrestrial LiDAR in natural forest environments. The STEP method deals with the acquisition artefacts of point clouds from terrestrial LiDAR which include three important constraints: a varying sampling rate, signal occlusion, and the presence of noise. The STEP method uses a combination of an original Hough transform and a new form of growing active contours (also referred to as “snakes”) to overcome these constraints while being able to handle large data sets. The framework proves to be resilient under various conditions as a general shape recognition and reconstruction tool. In the field of forestry, the method was demonstrated to be robust to the previously highlighted limitations (with errors in the range of manual forest measurements, that is 1 cm diameter error). The STEP method has therefore the potential to improve current forest inventories as well as being applied to a wide array of other applications, such as pipeline reconstruction and the assessment of industrial structures.

Searching combinatorial optimality using graph-based homology information

This paper analyses the topological information of a digital object

Surface reconstruction of incomplete datasets: A novel Poisson surface approach based on CSRBF

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Triangulation of an elevation surface structured by a sparse 3D grid

O under a combined combinatorial-algebraic point of view. Working with a topology-preserving cellularization

Terrain Model Reconstruction from Terrestrial LiDAR Data Using Radial Basis Functions

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