11 research outputs found
Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity
In this paper we present a scalable approach for robustly computing a 3D
surface mesh from multi-scale multi-view stereo point clouds that can handle
extreme jumps of point density (in our experiments three orders of magnitude).
The backbone of our approach is a combination of octree data partitioning,
local Delaunay tetrahedralization and graph cut optimization. Graph cut
optimization is used twice, once to extract surface hypotheses from local
Delaunay tetrahedralizations and once to merge overlapping surface hypotheses
even when the local tetrahedralizations do not share the same topology.This
formulation allows us to obtain a constant memory consumption per sub-problem
while at the same time retaining the density independent interpolation
properties of the Delaunay-based optimization. On multiple public datasets, we
demonstrate that our approach is highly competitive with the state-of-the-art
in terms of accuracy, completeness and outlier resilience. Further, we
demonstrate the multi-scale potential of our approach by processing a newly
recorded dataset with 2 billion points and a point density variation of more
than four orders of magnitude - requiring less than 9GB of RAM per process.Comment: This paper was accepted to the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 2017. The copyright was transfered to IEEE
(ieee.org). The official version of the paper will be made available on IEEE
Xplore (R) (ieeexplore.ieee.org). This version of the paper also contains the
supplementary material, which will not appear IEEE Xplore (R
Visual-Guided Mesh Repair
Mesh repair is a long-standing challenge in computer graphics and related
fields. Converting defective meshes into watertight manifold meshes can greatly
benefit downstream applications such as geometric processing, simulation,
fabrication, learning, and synthesis. In this work, we first introduce three
visual measures for visibility, orientation, and openness, based on
ray-tracing. We then present a novel mesh repair framework that incorporates
visual measures with several critical steps, i.e., open surface closing, face
reorientation, and global optimization, to effectively repair defective meshes,
including gaps, holes, self-intersections, degenerate elements, and
inconsistent orientations. Our method reduces unnecessary mesh complexity
without compromising geometric accuracy or visual quality while preserving
input attributes such as UV coordinates for rendering. We evaluate our approach
on hundreds of models randomly selected from ShapeNet and Thingi10K,
demonstrating its effectiveness and robustness compared to existing approaches
From 3D Models to 3D Prints: an Overview of the Processing Pipeline
Due to the wide diffusion of 3D printing technologies, geometric algorithms
for Additive Manufacturing are being invented at an impressive speed. Each
single step, in particular along the Process Planning pipeline, can now count
on dozens of methods that prepare the 3D model for fabrication, while analysing
and optimizing geometry and machine instructions for various objectives. This
report provides a classification of this huge state of the art, and elicits the
relation between each single algorithm and a list of desirable objectives
during Process Planning. The objectives themselves are listed and discussed,
along with possible needs for tradeoffs. Additive Manufacturing technologies
are broadly categorized to explicitly relate classes of devices and supported
features. Finally, this report offers an analysis of the state of the art while
discussing open and challenging problems from both an academic and an
industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and
Innovation action; Grant agreement N. 68044
Surveying and Three-Dimensional Modeling for Preservation and Structural Analysis of Cultural Heritage
Dense point clouds can be used for three important steps in structural analysis, in the field of cultural heritage, regardless of which instrument it was used for acquisition data.
Firstly, they allow deriving the geometric part of a finite element (FE) model automatically or semi-automatically. User input is mainly required to complement invisible parts and boundaries of the structure, and to assign meaningful approximate physical parameters.
Secondly, FE model obtained from point clouds can be used to estimate better and more precise parameters of the structural analysis, i.e., to train the FE model.
Finally, the definition of a correct Level of Detail about the three-dimensional model, deriving from the initial point cloud, can be used to define the limit beyond which the structural analysis is compromised, or anyway less precise.
In this work of research, this will be demonstrated using three different case studies of buildings, consisting mainly of masonry, measured through terrestrial laser scanning and photogrammetric acquisitions.
This approach is not a typical study for geomatics analysis, but its challenges allow studying benefits and limitations. The results and the proposed approaches could represent a step towards a multidisciplinary approach where Geomatics can play a critical role in the monitoring and civil engineering field.
Furthermore, through a geometrical reconstruction, different analyses and comparisons are possible, in order to evaluate how the numerical model is accurate. In fact, the discrepancies between the different results allow to evaluate how, from a geometric and simplified modeling, important details can be lost. This causes, for example, modifications in terms of mass and volume of the structure