112 research outputs found
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
Hybrid Bayesian Eigenobjects: Combining Linear Subspace and Deep Network Methods for 3D Robot Vision
We introduce Hybrid Bayesian Eigenobjects (HBEOs), a novel representation for
3D objects designed to allow a robot to jointly estimate the pose, class, and
full 3D geometry of a novel object observed from a single viewpoint in a single
practical framework. By combining both linear subspace methods and deep
convolutional prediction, HBEOs efficiently learn nonlinear object
representations without directly regressing into high-dimensional space. HBEOs
also remove the onerous and generally impractical necessity of input data
voxelization prior to inference. We experimentally evaluate the suitability of
HBEOs to the challenging task of joint pose, class, and shape inference on
novel objects and show that, compared to preceding work, HBEOs offer
dramatically improved performance in all three tasks along with several orders
of magnitude faster runtime performance.Comment: To appear in the International Conference on Intelligent Robots
(IROS) - Madrid, 201
Shape from Projections via Differentiable Forward Projector for Computed Tomography
In computed tomography, the reconstruction is typically obtained on a voxel
grid. In this work, however, we propose a mesh-based reconstruction method. For
tomographic problems, 3D meshes have mostly been studied to simulate data
acquisition, but not for reconstruction, for which a 3D mesh means the inverse
process of estimating shapes from projections. In this paper, we propose a
differentiable forward model for 3D meshes that bridge the gap between the
forward model for 3D surfaces and optimization. We view the forward projection
as a rendering process, and make it differentiable by extending recent work in
differentiable rendering. We use the proposed forward model to reconstruct 3D
shapes directly from projections. Experimental results for single-object
problems show that the proposed method outperforms traditional voxel-based
methods on noisy simulated data. We also apply the proposed method on electron
tomography images of nanoparticles to demonstrate the applicability of the
method on real data
Repairing geometric errors in 3D urban models with kinetic data structures
International audience3D urban models created either interactively by human operators or automatically with reconstruction algorithms often contain geometric and semantic errors. Correcting them in an automated manner is an important scientific challenge. Prior work, which traditionally relies on local analysis and heuristic-based geometric operations on mesh data structures, is typically tailored-made for specific 3D formats and urban objects. We propose a more general method to process different types of urban models without tedious parameter tuning. The key idea lies on the construction of a kinetic data structure that decomposes the 3D space into polyhedra by extending the facets of the imperfect input model. Such a data structure allows us to rebuild all the relations between the facets in an efficient and robust manner. Once built, the cells of the polyhedral partition are regrouped by semantic classes to reconstruct the corrected output model. We demonstrate the robustness and efficiency of our algorithm on a variety of real-world defect-laden models and show its competitiveness with respect to traditional mesh repairing techniques from both Building Information Modeling (BIM) and Geographic Information Systems (GIS) data
A Parallel Feature-preserving Mesh Variable Offsetting Method with Dynamic Programming
Mesh offsetting plays an important role in discrete geometric processing. In
this paper, we propose a parallel feature-preserving mesh offsetting framework
with variable distance. Different from the traditional method based on distance
and normal vector, a new calculation of offset position is proposed by using
dynamic programming and quadratic programming, and the sharp feature can be
preserved after offsetting. Instead of distance implicit field, a spatial
coverage region represented by polyhedral for computing offsets is proposed.
Our method can generate an offsetting model with smaller mesh size, and also
can achieve high quality without gaps, holes, and self-intersections. Moreover,
several acceleration techniques are proposed for the efficient mesh offsetting,
such as the parallel computing with grid, AABB tree and rays computing. In
order to show the efficiency and robustness of the proposed framework, we have
tested our method on the quadmesh dataset, which is available at
[https://www.quadmesh.cloud]. The source code of the proposed algorithm is
available on GitHub at [https://github.com/iGame-Lab/PFPOffset]
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
- …