57 research outputs found
A Bayesian Approach to Manifold Topology Reconstruction
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated
An Efficient Framework for Image Matching
AbstractThe goal of this paper is to present an efficient framework for non-rigid medical image matching. Previous non-rigid matching often produces unpredictable deformation field and unwanted stretching in the images. The as-rigid-as-possible nature of the Moving-LS technique thus makes it a new candidate by providing transformation that maintains the rigidity of structures for underlying physical reasons, while producing local deformations. In addition, it is very suitable for parallel computation, and the performance can be accelerated by multi-core processors through employment of multiple threads. The results demonstrate that the proposed matching method has good balance between accuracy and speed, and has potential in many medical applications
Differentiable Subdivision Surface Fitting
In this paper, we present a powerful differentiable surface fitting technique
to derive a compact surface representation for a given dense point cloud or
mesh, with application in the domains of graphics and CAD/CAM. We have chosen
the Loop subdivision surface, which in the limit yields the smooth surface
underlying the point cloud, and can handle complex surface topology better than
other popular compact representations, such as NURBS. The principal idea is to
fit the Loop subdivision surface not directly to the point cloud, but to the
IMLS (implicit moving least squares) surface defined over the point cloud. As
both Loop subdivision and IMLS have analytical expressions, we are able to
formulate the problem as an unconstrained minimization problem of a completely
differentiable function that can be solved with standard numerical solvers.
Differentiability enables us to integrate the subdivision surface into any deep
learning method for point clouds or meshes. We demonstrate the versatility and
potential of this approach by using it in conjunction with a differentiable
renderer to robustly reconstruct compact surface representations of
spatial-temporal sequences of dense meshes
A Bayesian Approach to Manifold Topology Reconstruction
In this paper, we investigate the problem of statistical reconstruction of piecewise linear manifold topology. Given a noisy, probably undersampled point cloud from a one- or two-manifold, the algorithm reconstructs an approximated most likely mesh in a Bayesian sense from which the sample might have been taken. We incorporate statistical priors on the object geometry to improve the reconstruction quality if additional knowledge about the class of original shapes is available. The priors can be formulated analytically or learned from example geometry with known manifold tessellation. The statistical objective function is approximated by a linear programming / integer programming problem, for which a globally optimal solution is found. We apply the algorithm to a set of 2D and 3D reconstruction examples, demon-strating that a statistics-based manifold reconstruction is feasible, and still yields plausible results in situations where sampling conditions are violated
Polyhedral Surface: Self-supervised Point Cloud Reconstruction Based on Polyhedral Surface
Point cloud reconstruction from raw point cloud has been an important topic
in computer graphics for decades, especially due to its high demand in modeling
and rendering applications. An important way to solve this problem is
establishing a local geometry to fit the local curve. However, previous methods
build either a local plane or polynomial curve. Local plane brings the loss of
sharp feature and the boundary artefacts on open surface. Polynomial curve is
hard to combine with neural network due to the local coordinate consistent
problem. To address this, we propose a novel polyhedral surface to represent
local surface. This method provides more flexible to represent sharp feature
and surface boundary on open surface. It does not require any local coordinate
system, which is important when introducing neural networks. Specifically, we
use normals to construct the polyhedral surface, including both dihedral and
trihedral surfaces using 2 and 3 normals, respectively. Our method achieves
state-of-the-art results on three commonly used datasets (ShapeNetCore, ABC,
and ScanNet). Code will be released upon acceptance
KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way
Robust and accurate pose estimation of a robotic platform, so-called
sensor-based odometry, is an essential part of many robotic applications. While
many sensor odometry systems made progress by adding more complexity to the
ego-motion estimation process, we move in the opposite direction. By removing a
majority of parts and focusing on the core elements, we obtain a surprisingly
effective system that is simple to realize and can operate under various
environmental conditions using different LiDAR sensors. Our odometry estimation
approach relies on point-to-point ICP combined with adaptive thresholding for
correspondence matching, a robust kernel, a simple but widely applicable motion
compensation approach, and a point cloud subsampling strategy. This yields a
system with only a few parameters that in most cases do not even have to be
tuned to a specific LiDAR sensor. Our system using the same parameters performs
on par with state-of-the-art methods under various operating conditions using
different platforms: automotive platforms, UAV-based operation, vehicles like
segways, or handheld LiDARs. We do not require integrating IMU information and
solely rely on 3D point cloud data obtained from a wide range of 3D LiDAR
sensors, thus, enabling a broad spectrum of different applications and
operating conditions. Our open-source system operates faster than the sensor
frame rate in all presented datasets and is designed for real-world scenarios.Comment: 8 page
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