StraightPCF: Straight Point Cloud Filtering

• URL: http://arxiv.org/abs/2405.08322v1
• Date: Tue, 14 May 2024 05:41:59 GMT
• Title: StraightPCF: Straight Point Cloud Filtering
• Authors: Dasith de Silva Edirimuni, Xuequan Lu, Gang Li, Lei Wei, Antonio Robles-Kelly, Hongdong Li,
• Abstract summary: Point cloud filtering is a fundamental 3D vision task, which aims to remove noise while recovering the underlying clean surfaces. We introduce StraightPCF, a new deep learning based method for point cloud filtering. It works by moving noisy points along straight paths, thus reducing discretization errors while ensuring faster convergence to the clean surfaces.
• Score: 50.66412286723848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud filtering is a fundamental 3D vision task, which aims to remove noise while recovering the underlying clean surfaces. State-of-the-art methods remove noise by moving noisy points along stochastic trajectories to the clean surfaces. These methods often require regularization within the training objective and/or during post-processing, to ensure fidelity. In this paper, we introduce StraightPCF, a new deep learning based method for point cloud filtering. It works by moving noisy points along straight paths, thus reducing discretization errors while ensuring faster convergence to the clean surfaces. We model noisy patches as intermediate states between high noise patch variants and their clean counterparts, and design the VelocityModule to infer a constant flow velocity from the former to the latter. This constant flow leads to straight filtering trajectories. In addition, we introduce a DistanceModule that scales the straight trajectory using an estimated distance scalar to attain convergence near the clean surface. Our network is lightweight and only has $\sim530K$ parameters, being 17% of IterativePFN (a most recent point cloud filtering network). Extensive experiments on both synthetic and real-world data show our method achieves state-of-the-art results. Our method also demonstrates nice distributions of filtered points without the need for regularization. The implementation code can be found at: https://github.com/ddsediri/StraightPCF.

Related papers

• Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors [53.6277160912059]
  We propose a method to promote pros of data-driven based and overfitting-based methods for better generalization, faster inference, and higher accuracy in learning neural SDFs. We introduce a novel statistical reasoning algorithm in local regions which is able to finetune data-driven based priors without signed distance supervision, clean point cloud, or point normals.
  arXiv  Detail & Related papers  (2024-10-25T16:48:44Z)
• Fast Learning of Signed Distance Functions from Noisy Point Clouds via Noise to Noise Mapping [54.38209327518066]
  Learning signed distance functions from point clouds is an important task in 3D computer vision. We propose to learn SDFs via a noise to noise mapping, which does not require any clean point cloud or ground truth supervision. 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 observations.
  arXiv  Detail & Related papers  (2024-07-04T03:35:02Z)
• Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling [55.05706827963042]
  Point cloud upsampling aims to generate dense and uniformly distributed point sets from a sparse point cloud. Previous methods typically split a sparse point cloud into several local patches, upsample patch points, and merge all upsampled patches. We propose a novel approach that learns an unsigned distance field guided by local priors for point cloud upsampling.
  arXiv  Detail & Related papers  (2023-12-23T01:52:14Z)
• Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler [103.97487121678276]
  Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs. We propose a novel Knowledge-driven Differential Filter Sampler(KDFS) with Masked Filter Modeling(MFM) framework for filter pruning.
  arXiv  Detail & Related papers  (2023-07-01T02:28:41Z)
• Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping [52.25114448281418]
  Learning signed distance functions (SDFs) from 3D point clouds is an important task in 3D computer vision. 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.
  arXiv  Detail & Related papers  (2023-06-02T09:52:04Z)
• IterativePFN: True Iterative Point Cloud Filtering [18.51768749680731]
  A fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising. We propose IterativePFN (iterative point cloud filtering network), which consists of multiple Iterations that model the true iterative filtering process internally. Our method is able to obtain better performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-04T04:47:44Z)
• Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering [12.602645108896636]
  We propose a novel deep learning method to jointly estimate normals and filter point clouds. We first introduce a 3D patch based contrastive learning framework, with noise corruption as an augmentation. Experimental results show that our method well supports the two tasks simultaneously and preserves sharp features and fine details.
  arXiv  Detail & Related papers  (2022-08-14T09:16:25Z)
• Towards Uniform Point Distribution in Feature-preserving Point Cloud Filtering [7.863178525014377]
  This paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering. Experiments show that our method yields better results with a more uniform point distribution ($5.8times10-5$ Chamfer Distance on average) in seconds.
  arXiv  Detail & Related papers  (2022-01-05T09:08:44Z)
• PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling [39.36827481232841]
  We present a novel end-to-end network for robust point clouds processing, named PointASNL. Key component in our approach is the adaptive sampling (AS) module. Our AS module can not only benefit the feature learning of point clouds, but also ease the biased effect of outliers.
  arXiv  Detail & Related papers  (2020-03-01T14:04:08Z)

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.