Parametric Surface Constrained Upsampler Network for Point Cloud

• URL: http://arxiv.org/abs/2303.08240v3
• Date: Sun, 3 Dec 2023 22:25:32 GMT
• Title: Parametric Surface Constrained Upsampler Network for Point Cloud
• Authors: Pingping Cai and Zhenyao Wu and Xinyi Wu and Song Wang
• Abstract summary: We introduce a novel surface regularizer into the upsampler network by forcing the neural network to learn the underlying parametric surface represented by bicubic functions and rotation functions. These designs are integrated into two different networks for two tasks that take advantages of upsampling layers. The state-of-the-art experimental results on both tasks demonstrate the effectiveness of the proposed method.
• Score: 33.033469444588086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing a point cloud upsampler, which aims to generate a clean and dense point cloud given a sparse point representation, is a fundamental and challenging problem in computer vision. A line of attempts achieves this goal by establishing a point-to-point mapping function via deep neural networks. However, these approaches are prone to produce outlier points due to the lack of explicit surface-level constraints. To solve this problem, we introduce a novel surface regularizer into the upsampler network by forcing the neural network to learn the underlying parametric surface represented by bicubic functions and rotation functions, where the new generated points are then constrained on the underlying surface. These designs are integrated into two different networks for two tasks that take advantages of upsampling layers - point cloud upsampling and point cloud completion for evaluation. The state-of-the-art experimental results on both tasks demonstrate the effectiveness of the proposed method. The code is available at https://github.com/corecai163/PSCU.

Related papers

• Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based Refinement [19.575833741231953]
  We use the KNN method to determine the neighborhoods of raw surface points. A conditional probability model is adaptive to local geometry, leading to significant rate reduction. We incorporate an implicit neural representation into the refinement layer, allowing the decoder to sample points on the underlying surface at arbitrary densities.
  arXiv  Detail & Related papers  (2024-08-06T05:24:06Z)
• 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)
• Arbitrary point cloud upsampling via Dual Back-Projection Network [12.344557879284219]
  We propose a Dual Back-Projection network for point cloud upsampling (DBPnet) A Dual Back-Projection is formulated in an up-down-up manner for point cloud upsampling. Experimental results show that the proposed method achieves the lowest point set matching losses.
  arXiv  Detail & Related papers  (2023-07-18T06:11:09Z)
• Learning Neural Volumetric Field for Point Cloud Geometry Compression [13.691147541041804]
  We propose to code the geometry of a given point cloud by learning a neural field. We divide the entire space into small cubes and represent each non-empty cube by a neural network and an input latent code. The network is shared among all the cubes in a single frame or multiple frames, to exploit the spatial and temporal redundancy.
  arXiv  Detail & Related papers  (2022-12-11T19:55:24Z)
• Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation [79.60988242843437]
  We propose a novel approach that achieves self-supervised and magnification-flexible point clouds upsampling simultaneously. Experimental results demonstrate that our self-supervised learning based scheme achieves competitive or even better performance than supervised learning based state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-18T07:18:25Z)
• PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling [56.463507980857216]
  We propose a generative adversarial network for point cloud upsampling. It can make the upsampled points evenly distributed on the underlying surface but also efficiently generate clean high frequency regions.
  arXiv  Detail & Related papers  (2022-03-02T07:47:46Z)
• SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization [52.20602782690776]
  It is expensive and tedious to obtain large scale paired sparse-canned point sets for training from real scanned sparse data. We propose a self-supervised point cloud upsampling network, named SPU-Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. We conduct various experiments on both synthetic and real-scanned datasets, and the results demonstrate that we achieve comparable performance to the state-of-the-art supervised methods.
  arXiv  Detail & Related papers  (2020-12-08T14:14:09Z)
• Self-Sampling for Neural Point Cloud Consolidation [83.31236364265403]
  We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud. We repeatedly self-sample the input point cloud with global subsets that are used to train a deep neural network. We demonstrate the ability to consolidate point sets from a variety of shapes, while eliminating outliers and noise.
  arXiv  Detail & Related papers  (2020-08-14T17:16:02Z)

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.