Score-Based Point Cloud Denoising

• URL: http://arxiv.org/abs/2107.10981v5
• Date: Wed, 28 Feb 2024 02:29:41 GMT
• Title: Score-Based Point Cloud Denoising
• Authors: Shitong Luo, Wei Hu
• Abstract summary: Point clouds acquired from scanning devices are often perturbed by noise, which affects downstream tasks such as surface reconstruction and analysis. We propose to increase the log-likelihood of each point from $p * n$ via gradient ascent -- iteratively updating each point's position. We derive objective functions for training the network and develop a denoising algorithm leveraging on the estimated scores.
• Score: 26.090445369658312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point clouds acquired from scanning devices are often perturbed by noise, which affects downstream tasks such as surface reconstruction and analysis. The distribution of a noisy point cloud can be viewed as the distribution of a set of noise-free samples $p(x)$ convolved with some noise model $n$, leading to $(p * n)(x)$ whose mode is the underlying clean surface. To denoise a noisy point cloud, we propose to increase the log-likelihood of each point from $p * n$ via gradient ascent -- iteratively updating each point's position. Since $p * n$ is unknown at test-time, and we only need the score (i.e., the gradient of the log-probability function) to perform gradient ascent, we propose a neural network architecture to estimate the score of $p * n$ given only noisy point clouds as input. We derive objective functions for training the network and develop a denoising algorithm leveraging on the estimated scores. Experiments demonstrate that the proposed model outperforms state-of-the-art methods under a variety of noise models, and shows the potential to be applied in other tasks such as point cloud upsampling. The code is available at \url{https://github.com/luost26/score-denoise}.

Related papers

• 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)
• StraightPCF: Straight Point Cloud Filtering [50.66412286723848]
  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.
  arXiv  Detail & Related papers  (2024-05-14T05:41:59Z)
• Towards Robust Few-shot Point Cloud Semantic Segmentation [57.075074484313]
  Few-shot point cloud semantic segmentation aims to train a model to quickly adapt to new unseen classes with only a handful of support set samples. We propose a Component-level Clean Noise Separation (CCNS) representation learning to learn discriminative feature representations. We also propose a Multi-scale Degree-based Noise Suppression (MDNS) scheme to remove the noisy shots from the support set.
  arXiv  Detail & Related papers  (2023-09-20T11:40:10Z)
• 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)
• 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)
• PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows [20.382995180671205]
  Point cloud denoising aims to restore clean point clouds from raw observations corrupted by noise and outliers. We present a novel deep learning-based denoising model, that incorporates normalizing flows and noise disentanglement techniques.
  arXiv  Detail & Related papers  (2022-03-11T14:17:58Z)
• 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)
• Differentiable Manifold Reconstruction for Point Cloud Denoising [23.33652755967715]
  3D point clouds are often perturbed by noise due to the inherent limitation of acquisition equipments. We propose to learn the underlying manifold of a noisy point cloud from differentiably subsampled points. We show that our method significantly outperforms state-of-the-art denoising methods under both synthetic noise and real world noise.
  arXiv  Detail & Related papers  (2020-07-27T13:31:41Z)
• 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.