Point Cloud Denoising

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GUARD: Geometric Uncertainty-Aware Point Cloud Denoising and Segmentation for Robotic Hard Disk Drive Disassembly
Oct 6, 2026Zuoxu Wang, Xiao LiangPoint Cloud Denoising Point Cloud Learning
Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843 USA
Denoising-Aware Temporal Point Cloud Completion for 3D Crop Architecture Recovery and Phenotypic Trait Extraction
Aug 28, 2026Mrudul Mittal, Soumyashree KarPoint Cloud Denoising Point Cloud Completion
Department of Electronics Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India · Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
RODR: Riemannian Orthogonally Decoupled Regularization for Disentangled Manifold Representation
Jul 27, 2026Jiayu Zhu, Wenlai ZhaoRiemannian Optimization Point Cloud Denoising
Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
SIMPC: Learning Self-Induced Mirror-Point Consistency for Unsupervised Point Cloud Denoising
May 26, 2026Chengwei Zhang, Xueyi Zhang, Tao Jiang +4Unsupervised Learning Point Cloud Denoising
National Key Laboratory of Microwave Imaging, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China · School of Computing, National University of Singapore, Singapore
PQDT: Pseudo-Query Dual Transformer for Robust Point Cloud Restoration
May 24, 2026Haoqing Wu, Alexa Nawotki, Jochen GarckePoint Cloud Completion 3D ViTs
Mercedes-Benz AG, Germany · University of Bonn, Germany · Fraunhofer SCAI, Germany
Super-Resolution of Airborne Laser Scanning Point Clouds for Forest Inventory
May 4, 2026Jinyuan Shao, Sangyoong Park, Chunxi Zhao +2Point Cloud Denoising Point Cloud Learning
Department of Forestry and Natural Resources, Purdue University, USA · Lyles School of Civil and Construction Engineering, Purdue University, USA
UGD: An Unsupervised Geometric Distance for Evaluating Real-world Noisy Point Cloud Denoising
Apr 18, 2026Zhiyong Su, Jincan Wu, Yonghui Liu +2Point Cloud Denoising Point Cloud Learning
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China · School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Deformation-based In-Context Learning for Point Cloud Understanding
Apr 3, 2026Chengxing Lin, Jinhong Deng, Yinjie Lei +1Point Cloud Denoising Point Cloud Learning
Shenzhen Institute for Advanced Study, UESTC · School of Computer Science and Engineering, UESTC · Sichuan University
Mamba Learns in Context: Structure-Aware Domain Generalization for Multi-Task Point Cloud Understanding
Mar 21, 2026Jincen Jiang, Qianyu Zhou, Yuhang Li +5Point Cloud Denoising Point Cloud Learning
Bournemouth University · Jilin University · The University of Western Australia +2
LIORNet: Self-Supervised LiDAR Snow Removal Framework for Autonomous Driving under Adverse Weather Conditions
Mar 20, 2026Ji-il Park, Inwook ShimPoint Cloud Denoising Autonomous Driving Perception
Ministry of National Defense, 22, Itaewon-ro, Yongsan-gu, Seoul, Republic of Korea. · Department of Smart Mobility Engineering, Inha University, Republic of Korea.

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GUARD: Geometric Uncertainty-Aware Point Cloud Denoising and Segmentation for Robotic Hard Disk Drive Disassembly

Zuoxu Wang, Xiao Liang

Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843 USA

Point Cloud Denoising Point Cloud Learning 3D Part Segmentation Segmentation Uncertainty Estimation

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Reliable robotic disassembly requires part-level representations that distinguish genuine component geometry from scanning and reconstruction artifacts. In point clouds of hard disk drives (HDDs), structured ghost artifacts can resemble valid components locally while remaining inconsistent with the overall geometry, allowing erroneous measurements to receive plausible semantic labels. This creates an engineering information problem: semantic prediction confidence alone does not establish whether the underlying geometry is reliable. We propose GUARD, a geometric uncertainty-aware framework that performs point filtering and segmentation within a single forward pass by modeling the reliability of learned geometric representations. GUARD combines a multi-scale geometric transformer with a multi-bandwidth random Fourier feature Gaussian Process to estimate per-point geometric uncertainty, complemented by predictive entropy to suppress unreliable measurements while preserving informative structures. Evaluation on 2,745 real HDD point clouds shows that GUARD improves PointNet++ segmentation mean intersection over union from 0.7739 to 0.8318. Additional experiments on ShapeNetPart and ScanNet examine robustness across corruption types, point-cloud domains, and segmentation backbones. On manually annotated ScanNet samples, geometric uncertainty achieves a corrupted-point detection F1 score of 0.7931, compared with 0.2212 for predictive entropy. The results demonstrate the value of distinguishing geometric reliability from semantic confidence and reveal a tradeoff between artifact suppression and preservation of informative structures. GUARD contributes a reliability-aware approach to interpreting imperfect 3D measurements for component identification and subsequent robotic handling. Project website: https://001-wang.github.io/GUARD_Point_denoiser/.