16 research outputs found
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning
We show that denoising of 3D point clouds can be learned unsupervised,
directly from noisy 3D point cloud data only. This is achieved by extending
recent ideas from learning of unsupervised image denoisers to unstructured 3D
point clouds. Unsupervised image denoisers operate under the assumption that a
noisy pixel observation is a random realization of a distribution around a
clean pixel value, which allows appropriate learning on this distribution to
eventually converge to the correct value. Regrettably, this assumption is not
valid for unstructured points: 3D point clouds are subject to total noise, i.
e., deviations in all coordinates, with no reliable pixel grid. Thus, an
observation can be the realization of an entire manifold of clean 3D points,
which makes a na\"ive extension of unsupervised image denoisers to 3D point
clouds impractical. Overcoming this, we introduce a spatial prior term, that
steers converges to the unique closest out of the many possible modes on a
manifold. Our results demonstrate unsupervised denoising performance similar to
that of supervised learning with clean data when given enough training examples
- whereby we do not need any pairs of noisy and clean training data.Comment: Proceedings of ICCV 201
SPARSE POINT CLOUD FILTERING BASED ON COVARIANCE FEATURES
Abstract. This work presents an extended photogrammetric pipeline aimed to improve 3D reconstruction results. Standard photogrammetric pipelines can produce noisy 3D data, especially when images are acquired with various sensors featuring different properties. In this paper, we propose an automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase. Bad 3D tie points and outliers are detected and removed, relying on micro and macro-clusters analyses. Clusters are built according to the prevalent dimensionality class (1D, 2D, 3D) assigned to low-entropy points, and corresponding to the main linear, planar o scatter local behaviour of the point cloud. While the macro-clusters analysis removes smallsized clusters and high-entropy points, in the micro-clusters investigation covariance features are used to verify the inner coherence of each point to the assigned class. Results on heritage scenarios are presented and discussed.</p
Sparse point cloud filtering based on covariance features
This work presents an extended photogrammetric pipeline aimed to improve 3D reconstruction results. Standard photogrammetric pipelines can produce noisy 3D data, especially when images are acquired with various sensors featuring different properties. In this paper, we propose an automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase. Bad 3D tie points and outliers are detected and removed, relying on micro and macro-clusters analyses. Clusters are built according to the prevalent dimensionality class (1D, 2D, 3D) assigned to low-entropy points, and corresponding to the main linear, planar o scatter local behaviour of the point cloud. While the macro-clusters analysis removes smallsized clusters and high-entropy points, in the micro-clusters investigation covariance features are used to verify the inner coherence of each point to the assigned class. Results on heritage scenarios are presented and discussed
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning
We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel observation is a random realization of a distribution around a clean pixel value, which allows appropriate learning on this distribution to eventually converge to the correct value. Regrettably, this assumption is not valid for unstructured points: 3D point clouds are subject to total noise, i.e. deviations in all coordinates, with no reliable pixel grid. Thus, an observation can be the realization of an entire manifold of clean 3D points, which makes the quality of a naive extension of unsupervised image denoisers to 3D point clouds unfortunately only little better than mean filtering. To overcome this, and to enable effective and unsupervised 3D point cloud denoising, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on the manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data
Learning Graph-Convolutional Representations for Point Cloud Denoising
Point clouds are an increasingly relevant data type but they are often
corrupted by noise. We propose a deep neural network based on
graph-convolutional layers that can elegantly deal with the
permutation-invariance problem encountered by learning-based point cloud
processing methods. The network is fully-convolutional and can build complex
hierarchies of features by dynamically constructing neighborhood graphs from
similarity among the high-dimensional feature representations of the points.
When coupled with a loss promoting proximity to the ideal surface, the proposed
approach significantly outperforms state-of-the-art methods on a variety of
metrics. In particular, it is able to improve in terms of Chamfer measure and
of quality of the surface normals that can be estimated from the denoised data.
We also show that it is especially robust both at high noise levels and in
presence of structured noise such as the one encountered in real LiDAR scans.Comment: European Conference on Computer Vision (ECCV) 202
IterativePFN: True Iterative Point Cloud Filtering
The quality of point clouds is often limited by noise introduced during their
capture process. Consequently, a fundamental 3D vision task is the removal of
noise, known as point cloud filtering or denoising. State-of-the-art learning
based methods focus on training neural networks to infer filtered displacements
and directly shift noisy points onto the underlying clean surfaces. In high
noise conditions, they iterate the filtering process. However, this iterative
filtering is only done at test time and is less effective at ensuring points
converge quickly onto the clean surfaces. We propose IterativePFN (iterative
point cloud filtering network), which consists of multiple IterationModules
that model the true iterative filtering process internally, within a single
network. We train our IterativePFN network using a novel loss function that
utilizes an adaptive ground truth target at each iteration to capture the
relationship between intermediate filtering results during training. This
ensures that the filtered results converge faster to the clean surfaces. Our
method is able to obtain better performance compared to state-of-the-art
methods. The source code can be found at:
https://github.com/ddsediri/IterativePFN.Comment: This paper has been accepted to the IEEE/CVF CVPR Conference, 202
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
Noise-Adaptive Shape Reconstruction from Raw Point Sets
International audienceWe propose a noise-adaptive shape reconstruction method specialized to smooth, closed shapes. Our algorithm takes as input a defect-laden point set with variable noise and outliers, and comprises three main steps. First, we compute a novel noise-adaptive distance function to the inferred shape, which relies on the assumption that the inferred shape is a smooth submanifold of known dimension. Second, we estimate the sign and confidence of the function at a set of seed points, through minimizing a quadratic energy expressed on the edges of a uniform random graph. Third, we compute a signed implicit function through a random walker approach with soft constraints chosen as the most confident seed points computed in previous step