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   Articles with "cloud denoising" as a keyword



MSaD-Net: A Mix Self-Attention Networks for 3D Point Cloud Denoising

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recommendations! Published in 2023 at "IEEE Photonics Journal"

DOI: 10.1109/jphot.2023.3272350

Abstract: In the process of acquiring 3D point cloud data, due to environmental interference or unstable scanning equipment, the acquired data often have noisy points. Recently, with the development of neural networks for point clouds, great… read more here.

Keywords: information; point cloud; cloud denoising; attention ... See more keywords

PN-Internet: Point-and-Normal Interactive Network for Noisy Point Clouds

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recommendations! Published in 2024 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2024.3395785

Abstract: Point cloud denoising and normal estimation are two fundamental yet dependent problems in digital geometry processing. However, both are often independently researched, leading to inconsistent geometry on 3-D surfaces. To address it, we propose point-and-normal… read more here.

Keywords: cloud denoising; point; point cloud; point normal ... See more keywords

Nonlocal Low-Rank Point Cloud Denoising for 3-D Measurement Surfaces

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recommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2021.3139686

Abstract: 3-D imaging devices (e.g., depth cameras and optical and laser scanners) are frequently used to measure outdoor/indoor scenes. The measurement data represented by 3-D point clouds are, however, usually noisy and should be denoised to… read more here.

Keywords: point cloud; cloud denoising; low rank; point ... See more keywords
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Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance

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recommendations! Published in 2021 at "IEEE Transactions on Image Processing"

DOI: 10.1109/tip.2021.3092826

Abstract: 3D dynamic point clouds provide a natural discrete representation of real-world objects or scenes in motion, with a wide range of applications in immersive telepresence, autonomous driving, surveillance, etc. Nevertheless, dynamic point clouds are often… read more here.

Keywords: dynamic point; distance; point; point cloud ... See more keywords

TripleMixer: A Triple-Domain Mixing Model for Point Cloud Denoising Under Adverse Weather

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recommendations! Published in 2025 at "IEEE Transactions on Image Processing"

DOI: 10.1109/tip.2025.3629047

Abstract: Adverse weather conditions such as snow, fog, and rain pose significant challenges to LiDAR-based perception models by introducing noise and corrupting point cloud measurements. To address this issue, we propose TripleMixer, a robust and efficient… read more here.

Keywords: cloud denoising; point; point cloud; weather ... See more keywords

PathNet: Path-Selective Point Cloud Denoising

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recommendations! Published in 2024 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"

DOI: 10.1109/tpami.2024.3355988

Abstract: Current point cloud denoising (PCD) models optimize single networks, trying to make their parameters adaptive to each point in a large pool of point clouds. Such a denoising network paradigm neglects that different points are… read more here.

Keywords: cloud denoising; path selective; point; point cloud ... See more keywords

LaPDA: Latent-Space Point Cloud Denoising With Adaptivity.

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recommendations! Published in 2025 at "IEEE transactions on visualization and computer graphics"

DOI: 10.1109/tvcg.2025.3635138

Abstract: Point cloud denoising is a fundamental yet challenging task in computer graphics. Existing solutions typically rely on supervised training on synthesized noise. However, real-world noise often exhibits greater complexity, causing learning-based methods trained on synthetic… read more here.

Keywords: cloud denoising; latent space; point cloud; noise ... See more keywords

Adaptive and Iterative Point Cloud Denoising with Score‐Based Diffusion Model

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recommendations! Published in 2025 at "Computer Graphics Forum"

DOI: 10.1111/cgf.70149

Abstract: Point cloud denoising task aims to recover the clean point cloud from the scanned data coupled with different levels or patterns of noise. The recent state‐of‐the‐art methods often train deep neural networks to update the… read more here.

Keywords: adaptive iterative; cloud denoising; point; point cloud ... See more keywords