Patch-based Progressive 3D Point Set Upsampling

We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-the-art learning-based and optimazation-based approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details.Comment: accepted to cvpr2019, code available at https://github.com/yifita/P3

Iterative consolidation on unorganized point clouds and its application in design.

Chan, Kwan Chung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (leaves 63-69).Abstracts in English and Chinese.Abstract --- p.vAcknowledgements --- p.ixList of Figures --- p.xiiiList of Tables --- p.xvChapter 1 --- Introduction --- p.1Chapter 1.1 --- Main contributions --- p.4Chapter 1.2 --- Overview --- p.4Chapter 2 --- Related Work --- p.7Chapter 2.1 --- Point cloud processing --- p.7Chapter 2.2 --- Model repairing --- p.9Chapter 2.3 --- Deformation and reconstruction --- p.10Chapter 3 --- Iterative Consolidation on Un-orientated Point Clouds --- p.11Chapter 3.1 --- Algorithm overview --- p.12Chapter 3.2 --- Down-sampling and outliers removal --- p.14Chapter 3.2.1 --- Normal estimation --- p.14Chapter 3.2.2 --- Down-sampling --- p.15Chapter 3.2.3 --- Particle noise removal --- p.17Chapter 3.3 --- APSS based repulsion --- p.19Chapter 3.4 --- Refinement --- p.22Chapter 3.4.1 --- Adaptive up-sampling --- p.22Chapter 3.4.2 --- Selection of up-sampled points --- p.23Chapter 3.4.3 --- Sample noise removal --- p.23Chapter 3.5 --- Set constraints to sample points --- p.24Chapter 4 --- Shape Modeling by Point Set --- p.27Chapter 4.1 --- Principle of deformation --- p.27Chapter 4.2 --- Selection --- p.29Chapter 4.3 --- Stretching and compressing --- p.30Chapter 4.4 --- Bending and twisting --- p.30Chapter 4.5 --- Inserting points --- p.30Chapter 5 --- Results and Discussion --- p.37Chapter 5.1 --- Program environment --- p.37Chapter 5.2 --- Results of iterative consolidation on un-orientated points --- p.37Chapter 5.3 --- Effect of our de-noising based on up-sampled points --- p.44Chapter 6 --- Conclusions --- p.49Chapter 6.1 --- Advantages --- p.49Chapter 6.2 --- Factors affecting our algorithm --- p.50Chapter 6.3 --- Possible future works --- p.51Chapter 6.3.1 --- Improve on the quality of results --- p.51Chapter 6.3.2 --- Reduce user input --- p.52Chapter 6.3.3 --- Multi-thread computation --- p.52Chapter A --- Finding Neighbors --- p.53Chapter A.1 --- k-d Tree --- p.53Chapter A.2 --- Octree --- p.54Chapter A.3 --- Minimum spanning tree --- p.55Chapter B --- Principle Component Analysis --- p.57Chapter B.1 --- Principle component analysis --- p.57Chapter C --- UI of the program --- p.59Chapter C.1 --- User Interface --- p.59Chapter D --- Publications --- p.61Bibliography --- p.6

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 $\lambda$-projection method, which means to project the noisy points onto the surface with an individual control coefficient $\lambda_{i}$ 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

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

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