101,768 research outputs found
Feature-assisted interactive geometry reconstruction in 3D point clouds using incremental region growing
Reconstructing geometric shapes from point clouds is a common task that is
often accomplished by experts manually modeling geometries in CAD-capable
software. State-of-the-art workflows based on fully automatic geometry
extraction are limited by point cloud density and memory constraints, and
require pre- and post-processing by the user. In this work, we present a
framework for interactive, user-driven, feature-assisted geometry
reconstruction from arbitrarily sized point clouds. Based on seeded
region-growing point cloud segmentation, the user interactively extracts planar
pieces of geometry and utilizes contextual suggestions to point out plane
surfaces, normal and tangential directions, and edges and corners. We implement
a set of feature-assisted tools for high-precision modeling tasks in
architecture and urban surveying scenarios, enabling instant-feedback
interactive point cloud manipulation on large-scale data collected from
real-world building interiors and facades. We evaluate our results through
systematic measurement of the reconstruction accuracy, and interviews with
domain experts who deploy our framework in a commercial setting and give both
structured and subjective feedback.Comment: 13 pages, submitted to Computers & Graphics Journa
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
This paper addresses the problem of generating uniform dense point clouds to
describe the underlying geometric structures from given sparse point clouds.
Due to the irregular and unordered nature, point cloud densification as a
generative task is challenging. To tackle the challenge, we propose a novel
deep neural network based method, called PUGeo-Net, that learns a
linear transformation matrix for each input point. Matrix
approximates the augmented Jacobian matrix of a local parameterization and
builds a one-to-one correspondence between the 2D parametric domain and the 3D
tangent plane so that we can lift the adaptively distributed 2D samples (which
are also learned from data) to 3D space. After that, we project the samples to
the curved surface by computing a displacement along the normal of the tangent
plane. PUGeo-Net is fundamentally different from the existing deep learning
methods that are largely motivated by the image super-resolution techniques and
generate new points in the abstract feature space. Thanks to its
geometry-centric nature, PUGeo-Net works well for both CAD models with sharp
features and scanned models with rich geometric details. Moreover, PUGeo-Net
can compute the normal for the original and generated points, which is highly
desired by the surface reconstruction algorithms. Computational results show
that PUGeo-Net, the first neural network that can jointly generate vertex
coordinates and normals, consistently outperforms the state-of-the-art in terms
of accuracy and efficiency for upsampling factor .Comment: 17 pages, 10 figure
3D Geometric Analysis of Tubular Objects based on Surface Normal Accumulation
This paper proposes a simple and efficient method for the reconstruction and
extraction of geometric parameters from 3D tubular objects. Our method
constructs an image that accumulates surface normal information, then peaks
within this image are located by tracking. Finally, the positions of these are
optimized to lie precisely on the tubular shape centerline. This method is very
versatile, and is able to process various input data types like full or partial
mesh acquired from 3D laser scans, 3D height map or discrete volumetric images.
The proposed algorithm is simple to implement, contains few parameters and can
be computed in linear time with respect to the number of surface faces. Since
the extracted tube centerline is accurate, we are able to decompose the tube
into rectilinear parts and torus-like parts. This is done with a new linear
time 3D torus detection algorithm, which follows the same principle of a
previous work on 2D arc circle recognition. Detailed experiments show the
versatility, accuracy and robustness of our new method.Comment: in 18th International Conference on Image Analysis and Processing,
Sep 2015, Genova, Italy. 201
Parametric Surfaces for Augmented Architecture representation
Augmented Reality (AR) represents a growing communication channel, responding to the need to expand reality with additional information, offering easy and engaging access to digital data. AR for architectural representation allows a simple interaction with 3D models, facilitating spatial understanding of complex volumes and topological relationships between parts, overcoming some limitations related to Virtual Reality. In the last decade different developments in the pipeline process have seen a significant advancement in technological and algorithmic aspects, paying less attention to 3D modeling generation. For this, the article explores the construction of basic geometries for 3D model’s generation, highlighting the relationship between geometry and topology, basic for a consistent normal distribution. Moreover, a critical evaluation about corrective paths of existing 3D models is presented, analysing a complex architectural case study, the virtual model of Villa del Verginese, an emblematic example for topological emerged problems. The final aim of the paper is to refocus attention on 3D model construction, suggesting some "good practices" useful for preventing, minimizing or correcting topological problems, extending the accessibility of AR to people engaged in architectural representation
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