91 research outputs found
A framework for hull form reverse engineering and geometry integration into numerical simulations
The thesis presents a ship hull form specific reverse engineering and CAD integration framework. The reverse engineering part proposes three alternative suitable reconstruction approaches namely curves network, direct surface fitting, and triangulated surface reconstruction. The CAD integration part includes surface healing, region identification, and domain preparation strategies which used to adapt the CAD model to downstream application requirements. In general, the developed framework bridges a point cloud and a CAD model obtained from IGES and STL file into downstream applications
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
Brain mapping research in most neuroanatomical laboratories relies on
conventional processing techniques, which often introduce histological
artifacts such as tissue tears and tissue loss. In this paper we present
techniques and algorithms for automatic registration and 3D reconstruction of
conventionally produced mouse brain slices in a standardized atlas space. This
is achieved first by constructing a virtual 3D mouse brain model from annotated
slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed
model generates ARA-based slice images corresponding to the microscopic images
of histological brain sections. These image pairs are aligned using a geometric
approach through contour images. Histological artifacts in the microscopic
images are detected and removed using Constrained Delaunay Triangulation before
performing global alignment. Finally, non-linear registration is performed by
solving Laplace's equation with Dirichlet boundary conditions. Our methods
provide significant improvements over previously reported registration
techniques for the tested slices in 3D space, especially on slices with
significant histological artifacts. Further, as an application we count the
number of neurons in various anatomical regions using a dataset of 51
microscopic slices from a single mouse brain. This work represents a
significant contribution to this subfield of neuroscience as it provides tools
to neuroanatomist for analyzing and processing histological data.Comment: 14 pages, 11 figure
TVL<sub>1</sub> Planarity Regularization for 3D Shape Approximation
The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within.
This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy
Polylidar3D -- Fast Polygon Extraction from 3D Data
Flat surfaces captured by 3D point clouds are often used for localization,
mapping, and modeling. Dense point cloud processing has high computation and
memory costs making low-dimensional representations of flat surfaces such as
polygons desirable. We present Polylidar3D, a non-convex polygon extraction
algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data),
organized point clouds (e.g., range images), or user-provided meshes.
Non-convex polygons represent flat surfaces in an environment with interior
cutouts representing obstacles or holes. The Polylidar3D front-end transforms
input data into a half-edge triangular mesh. This representation provides a
common level of input data abstraction for subsequent back-end processing. The
Polylidar3D back-end is composed of four core algorithms: mesh smoothing,
dominant plane normal estimation, planar segment extraction, and finally
polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU
multi-threading and GPU acceleration when available. We demonstrate
Polylidar3D's versatility and speed with real-world datasets including aerial
LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds
for road surface detection, and RGBD cameras for indoor floor/wall detection.
We also evaluate Polylidar3D on a challenging planar segmentation benchmark
dataset. Results consistently show excellent speed and accuracy.Comment: 40 page
Surface reconstruction by computing restricted Voronoi cells in parallel
International audienceWe present a method for reconstructing a 3D surface triangulation from an input point set. The main component of the method is an algorithm that computes the restricted Voronoi diagram. In our specific case, it corresponds to the intersection between the 3D Voronoi diagram of the input points and a set of disks centered at the points and orthogonal to the estimated normal directions. The method does not require coherent normal orientations (just directions). Our algorithm is based on a property of the restricted Voronoi cells that leads to an embarrassingly parallel implementation. We experimented our algorithm with scanned point sets with up to 100 million vertices that were processed within few minutes on a standard computer. The complete implementation is provided
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