Weighted Poisson-disk Resampling on Large-Scale Point Clouds

• URL: http://arxiv.org/abs/2412.09177v2
• Date: Mon, 16 Dec 2024 05:51:27 GMT
• Title: Weighted Poisson-disk Resampling on Large-Scale Point Clouds
• Authors: Xianhe Jiao, Chenlei Lv, Junli Zhao, Ran Yi, Yu-Hui Wen, Zhenkuan Pan, Zhongke Wu, Yong-jin Liu,
• Abstract summary: We propose a weighted Poisson-disk (WPD) resampling method to improve the usability and efficiency for the processing.<n> Experiments show that our method significantly improves the performance of large-scale point cloud resampling for different applications.
• Score: 40.24901320665608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency. % in related tasks. However, current methods cannot balance such different requirements. Particularly with large-scale point clouds, classical methods often struggle with decreased efficiency and accuracy. To address such issues, we propose a weighted Poisson-disk (WPD) resampling method to improve the usability and efficiency for the processing. We first design an initial Poisson resampling with a voxel-based estimation strategy. It is able to estimate a more accurate radius of the Poisson-disk while maintaining high efficiency. Then, we design a weighted tangent smoothing step to further optimize the Voronoi diagram for each point. At the same time, sharp features are detected and kept in the optimized results with isotropic property. Finally, we achieve a resampling copy from the original point cloud with the specified point number, uniform density, and high-quality geometric consistency. Experiments show that our method significantly improves the performance of large-scale point cloud resampling for different applications, and provides a highly practical solution.

Related papers

• Iterative Differential Entropy Minimization (IDEM) method for fine rigid pairwise 3D Point Cloud Registration: A Focus on the Metric [0.0]
  Authors introduce a novel differential entropy-based metric for fine rigid pairwise 3D point cloud registration.<n>The proposed metric proves effective even with density differences, noise, holes, and partial overlap, where RMSE does not always yield optimal alignment.
  arXiv  Detail & Related papers  (2026-01-14T16:16:51Z)
• Efficient Point Clouds Upsampling via Flow Matching [16.948354780275388]
  Existing diffusion models struggle with inefficiencies as they map Gaussian noise to real point clouds. We propose PUFM, a flow matching approach to directly map sparse point clouds to their high-fidelity dense counterparts. Our method delivers superior upsampling quality but with fewer sampling steps.
  arXiv  Detail & Related papers  (2025-01-25T17:50:53Z)
• Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning [52.825441454264585]
  We propose an arbitrary-scale Point cloud Upsampling framework using Voxel-based Network (textbfPU-VoxelNet) Thanks to the completeness and regularity inherited from the voxel representation, voxel-based networks are capable of providing predefined grid space to approximate 3D surface. A density-guided grid resampling method is developed to generate high-fidelity points while effectively avoiding sampling outliers.
  arXiv  Detail & Related papers  (2024-03-08T07:31:14Z)
• Point Cloud Pre-training with Diffusion Models [62.12279263217138]
  We propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif) PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-25T08:10:05Z)
• Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions [77.32043242988738]
  We propose a new framework for accurate point cloud upsampling that supports arbitrary upsampling rates. Our method first interpolates the low-res point cloud according to a given upsampling rate.
  arXiv  Detail & Related papers  (2023-04-24T06:36:35Z)
• Deep Geometry Post-Processing for Decompressed Point Clouds [32.72083309729585]
  Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission. We propose a novel learning-based post-processing method to enhance the decompressed point clouds. Experimental results show that the proposed method can significantly improve the quality of the decompressed point clouds.
  arXiv  Detail & Related papers  (2022-04-29T08:57:03Z)
• AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds [31.641383879577894]
  This paper presents a neural network for robust normal estimation on point clouds, named AdaFit, that can deal with point clouds with noise and density variations. Existing works use a network to learn point-wise weights for weighted least surface fitting to estimate the normals. We propose a simple yet effective solution that adds an additional offset prediction to improve the quality of normal estimation.
  arXiv  Detail & Related papers  (2021-08-12T16:37:24Z)
• PU-Flow: a Point Cloud Upsampling Networkwith Normalizing Flows [58.96306192736593]
  We present PU-Flow, which incorporates normalizing flows and feature techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we formulate the upsampling process as point in a latent space, where the weights are adaptively learned from local geometric context. We show that our method outperforms state-of-the-art deep learning-based approaches in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency.
  arXiv  Detail & Related papers  (2021-07-13T07:45:48Z)
• Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
  We propose a novel end-to-end learning-based framework to generate dense point clouds. We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
  arXiv  Detail & Related papers  (2020-11-25T14:00:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.