15,520 research outputs found
Alternately denoising and reconstructing unoriented point sets
We propose a new strategy to bridge point cloud denoising and surface
reconstruction by alternately updating the denoised point clouds and the
reconstructed surfaces. In Poisson surface reconstruction, the implicit
function is generated by a set of smooth basis functions centered at the
octnodes. When the octree depth is properly selected, the reconstructed surface
is a good smooth approximation of the noisy point set. Our method projects the
noisy points onto the surface and alternately reconstructs and projects the
point set. We use the iterative Poisson surface reconstruction (iPSR) to
support unoriented surface reconstruction. Our method iteratively performs iPSR
and acts as an outer loop of iPSR. Considering that the octree depth
significantly affects the reconstruction results, we propose an adaptive depth
selection strategy to ensure an appropriate depth choice. To manage the
oversmoothing phenomenon near the sharp features, we propose a
-projection method, which means to project the noisy points onto the
surface with an individual control coefficient for each point.
The coefficients are determined through a Voronoi-based feature detection
method. Experimental results show that our method achieves high performance in
point cloud denoising and unoriented surface reconstruction within different
noise scales, and exhibits well-rounded performance in various types of inputs.
The source code is available
at~\url{https://github.com/Submanifold/AlterUpdate}.Comment: Accepted by Computers & Graphics from CAD/Graphics 202
Finite Element Based Tracking of Deforming Surfaces
We present an approach to robustly track the geometry of an object that
deforms over time from a set of input point clouds captured from a single
viewpoint. The deformations we consider are caused by applying forces to known
locations on the object's surface. Our method combines the use of prior
information on the geometry of the object modeled by a smooth template and the
use of a linear finite element method to predict the deformation. This allows
the accurate reconstruction of both the observed and the unobserved sides of
the object. We present tracking results for noisy low-quality point clouds
acquired by either a stereo camera or a depth camera, and simulations with
point clouds corrupted by different error terms. We show that our method is
also applicable to large non-linear deformations.Comment: additional experiment
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
Point clouds obtained with 3D scanners or by image-based reconstruction
techniques are often corrupted with significant amount of noise and outliers.
Traditional methods for point cloud denoising largely rely on local surface
fitting (e.g., jets or MLS surfaces), local or non-local averaging, or on
statistical assumptions about the underlying noise model. In contrast, we
develop a simple data-driven method for removing outliers and reducing noise in
unordered point clouds. We base our approach on a deep learning architecture
adapted from PCPNet, which was recently proposed for estimating local 3D shape
properties in point clouds. Our method first classifies and discards outlier
samples, and then estimates correction vectors that project noisy points onto
the original clean surfaces. The approach is efficient and robust to varying
amounts of noise and outliers, while being able to handle large densely-sampled
point clouds. In our extensive evaluation, both on synthesic and real data, we
show an increased robustness to strong noise levels compared to various
state-of-the-art methods, enabling accurate surface reconstruction from
extremely noisy real data obtained by range scans. Finally, the simplicity and
universality of our approach makes it very easy to integrate in any existing
geometry processing pipeline
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
Learning signed distance functions (SDFs) from 3D point clouds is an
important task in 3D computer vision. However, without ground truth signed
distances, point normals or clean point clouds, current methods still struggle
from learning SDFs from noisy point clouds. To overcome this challenge, we
propose to learn SDFs via a noise to noise mapping, which does not require any
clean point cloud or ground truth supervision for training. Our novelty lies in
the noise to noise mapping which can infer a highly accurate SDF of a single
object or scene from its multiple or even single noisy point cloud
observations. Our novel learning manner is supported by modern Lidar systems
which capture multiple noisy observations per second. We achieve this by a
novel loss which enables statistical reasoning on point clouds and maintains
geometric consistency although point clouds are irregular, unordered and have
no point correspondence among noisy observations. Our evaluation under the
widely used benchmarks demonstrates our superiority over the state-of-the-art
methods in surface reconstruction, point cloud denoising and upsampling. Our
code, data, and pre-trained models are available at
https://github.com/mabaorui/Noise2NoiseMapping/Comment: To appear at ICML2023. Code and data are available at
https://github.com/mabaorui/Noise2NoiseMapping
Implicit reconstructions of thin leaf surfaces from large, noisy point clouds
Thin surfaces, such as the leaves of a plant, pose a significant challenge
for implicit surface reconstruction techniques, which typically assume a
closed, orientable surface. We show that by approximately interpolating a point
cloud of the surface (augmented with off-surface points) and restricting the
evaluation of the interpolant to a tight domain around the point cloud, we need
only require an orientable surface for the reconstruction. We use polyharmonic
smoothing splines to fit approximate interpolants to noisy data, and a
partition of unity method with an octree-like strategy for choosing subdomains.
This method enables us to interpolate an N-point dataset in O(N) operations. We
present results for point clouds of capsicum and tomato plants, scanned with a
handheld device. An important outcome of the work is that sufficiently smooth
leaf surfaces are generated that are amenable for droplet spreading
simulations
Point Normal Orientation and Surface Reconstruction by Incorporating Isovalue Constraints to Poisson Equation
Oriented normals are common pre-requisites for many geometric algorithms
based on point clouds, such as Poisson surface reconstruction. However, it is
not trivial to obtain a consistent orientation. In this work, we bridge
orientation and reconstruction in implicit space and propose a novel approach
to orient point clouds by incorporating isovalue constraints to the Poisson
equation. Feeding a well-oriented point cloud into a reconstruction approach,
the indicator function values of the sample points should be close to the
isovalue. Based on this observation and the Poisson equation, we propose an
optimization formulation that combines isovalue constraints with local
consistency requirements for normals. We optimize normals and implicit
functions simultaneously and solve for a globally consistent orientation. Owing
to the sparsity of the linear system, an average laptop can be used to run our
method within reasonable time. Experiments show that our method can achieve
high performance in non-uniform and noisy data and manage varying sampling
densities, artifacts, multiple connected components, and nested surfaces
StarNet: Style-Aware 3D Point Cloud Generation
This paper investigates an open research task of reconstructing and
generating 3D point clouds. Most existing works of 3D generative models
directly take the Gaussian prior as input for the decoder to generate 3D point
clouds, which fail to learn disentangled latent codes, leading noisy
interpolated results. Most of the GAN-based models fail to discriminate the
local geometries, resulting in the point clouds generated not evenly
distributed at the object surface, hence degrading the point cloud generation
quality. Moreover, prevailing methods adopt computation-intensive frameworks,
such as flow-based models and Markov chains, which take plenty of time and
resources in the training phase. To resolve these limitations, this paper
proposes a unified style-aware network architecture combining both point-wise
distance loss and adversarial loss, StarNet which is able to reconstruct and
generate high-fidelity and even 3D point clouds using a mapping network that
can effectively disentangle the Gaussian prior from input's high-level
attributes in the mapped latent space to generate realistic interpolated
objects. Experimental results demonstrate that our framework achieves
comparable state-of-the-art performance on various metrics in the point cloud
reconstruction and generation tasks, but is more lightweight in model size,
requires much fewer parameters and less time for model training
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