4 research outputs found
Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption
In this paper, we present a novel pose normalization method for indoor
mapping point clouds and triangle meshes that is robust against large fractions
of the indoor mapping geometries deviating from an ideal Manhattan World
structure. In the case of building structures that contain multiple Manhattan
World systems, the dominant Manhattan World structure supported by the largest
fraction of geometries is determined and used for alignment. In a first step, a
vertical alignment orienting a chosen axis to be orthogonal to horizontal floor
and ceiling surfaces is conducted. Subsequently, a rotation around the
resulting vertical axis is determined that aligns the dataset horizontally with
the coordinate axes. The proposed method is evaluated quantitatively against
several publicly available indoor mapping datasets. Our implementation of the
proposed procedure along with code for reproducing the evaluation will be made
available to the public upon acceptance for publication
A rectilinearity measurement for 3d meshes
In this paper, we propose and evaluate a novel shape measurement describing the extent to which a 3D mesh is rectilinear. Since the rectilinearity measure corresponds proportionally to the ratio of the sum of three orthogonal projected areas and the surface area of the mesh, it has the following desirable properties: 1) the estimated rectilinearity is always a number from (0,1]; 2) the estimated rectilinearity is 1 if and only if the measured 3D shape is rectilinear; 3) there are shapes whose estimated rectilinearity is arbitrarily close to 0; 4) the measurement is invariant under scale, rotation, and translation; 5) the 3D objects can be either open or closed meshes, and we can also deal with poor quality meshes; 6) the measurement is insensitive to noise and stable under small topology errors; and 7) a Genetic Algorithm (GA) can be applied to calculate the approximate rectilinearity efficiently. We have also implemented two experiments of its applications. The first experiment shows that, in some cases, the calculation of rectilinearity provides a better tool for registering the pose of 3D meshes compared to PCA. The second experiment demonstrates that the combination of this measurement and other shape descriptors can significantly improve 3D shape retrieval performance