4,538 research outputs found
SurfelMeshing: Online Surfel-Based Mesh Reconstruction
We address the problem of mesh reconstruction from live RGB-D video, assuming
a calibrated camera and poses provided externally (e.g., by a SLAM system). In
contrast to most existing approaches, we do not fuse depth measurements in a
volume but in a dense surfel cloud. We asynchronously (re)triangulate the
smoothed surfels to reconstruct a surface mesh. This novel approach enables to
maintain a dense surface representation of the scene during SLAM which can
quickly adapt to loop closures. This is possible by deforming the surfel cloud
and asynchronously remeshing the surface where necessary. The surfel-based
representation also naturally supports strongly varying scan resolution. In
particular, it reconstructs colors at the input camera's resolution. Moreover,
in contrast to many volumetric approaches, ours can reconstruct thin objects
since objects do not need to enclose a volume. We demonstrate our approach in a
number of experiments, showing that it produces reconstructions that are
competitive with the state-of-the-art, and we discuss its advantages and
limitations. The algorithm (excluding loop closure functionality) is available
as open source at https://github.com/puzzlepaint/surfelmeshing .Comment: Version accepted to IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view
images is a fundamental yet active research area in computer vision. Despite
the steady progress in multi-view stereo reconstruction, most existing methods
are still limited in recovering fine-scale details and sharp features while
suppressing noises, and may fail in reconstructing regions with few textures.
To address these limitations, this paper presents a Detail-preserving and
Content-aware Variational (DCV) multi-view stereo method, which reconstructs
the 3D surface by alternating between reprojection error minimization and mesh
denoising. In reprojection error minimization, we propose a novel inter-image
similarity measure, which is effective to preserve fine-scale details of the
reconstructed surface and builds a connection between guided image filtering
and image registration. In mesh denoising, we propose a content-aware
-minimization algorithm by adaptively estimating the value and
regularization parameters based on the current input. It is much more promising
in suppressing noise while preserving sharp features than conventional
isotropic mesh smoothing. Experimental results on benchmark datasets
demonstrate that our DCV method is capable of recovering more surface details,
and obtains cleaner and more accurate reconstructions than state-of-the-art
methods. In particular, our method achieves the best results among all
published methods on the Middlebury dino ring and dino sparse ring datasets in
terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image
processin
A moving mesh method for one-dimensional hyperbolic conservation laws
We develop an adaptive method for solving one-dimensional systems of hyperbolic conservation laws that employs a high resolution Godunov-type scheme for the physical equations, in conjunction with a moving mesh PDE governing the motion of the spatial grid points. Many other moving mesh methods developed to solve hyperbolic problems use a fully implicit discretization for the coupled solution-mesh equations, and so suffer from a significant degree of numerical stiffness. We employ a semi-implicit approach that couples the moving mesh equation to an efficient, explicit solver for the physical PDE, with the resulting scheme behaving in practice as a two-step predictor-corrector method. In comparison with computations on a fixed, uniform mesh, our method exhibits more accurate resolution of discontinuities for a similar level of computational work
EIT Reconstruction Algorithms: Pitfalls, Challenges and Recent Developments
We review developments, issues and challenges in Electrical Impedance
Tomography (EIT), for the 4th Workshop on Biomedical Applications of EIT,
Manchester 2003. We focus on the necessity for three dimensional data
collection and reconstruction, efficient solution of the forward problem and
present and future reconstruction algorithms. We also suggest common pitfalls
or ``inverse crimes'' to avoid.Comment: A review paper for the 4th Workshop on Biomedical Applications of
EIT, Manchester, UK, 200
Semantically Informed Multiview Surface Refinement
We present a method to jointly refine the geometry and semantic segmentation
of 3D surface meshes. Our method alternates between updating the shape and the
semantic labels. In the geometry refinement step, the mesh is deformed with
variational energy minimization, such that it simultaneously maximizes
photo-consistency and the compatibility of the semantic segmentations across a
set of calibrated images. Label-specific shape priors account for interactions
between the geometry and the semantic labels in 3D. In the semantic
segmentation step, the labels on the mesh are updated with MRF inference, such
that they are compatible with the semantic segmentations in the input images.
Also, this step includes prior assumptions about the surface shape of different
semantic classes. The priors induce a tight coupling, where semantic
information influences the shape update and vice versa. Specifically, we
introduce priors that favor (i) adaptive smoothing, depending on the class
label; (ii) straightness of class boundaries; and (iii) semantic labels that
are consistent with the surface orientation. The novel mesh-based
reconstruction is evaluated in a series of experiments with real and synthetic
data. We compare both to state-of-the-art, voxel-based semantic 3D
reconstruction, and to purely geometric mesh refinement, and demonstrate that
the proposed scheme yields improved 3D geometry as well as an improved semantic
segmentation
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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