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AGSM–CPA: Reliability-Aware Robustness for Rotation-Invariant Point Cloud Learning

Rotation-invariant (RI) point cloud models aim to reduce sensitivity to viewpoint changes, but their performance still drops noticeably in real-world settings when local geometry is degraded by noise, occlusion, and uneven sampling. Once these disturbances propagate through deeper layers, they can l...

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Principais autores: Mengyuan Ge, Shuocheng Wang, Yong Yang, Junfeng Yao
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2026-01-01
Serier:Mathematics
Fag:
rotation-invariant point cloud learning
robust feature extraction
geometric reliability modeling
consistency learning
noise perturbation
3D classification
Online adgang:https://www.mdpi.com/2227-7390/14/2/278
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