Jiaxun Guo — accepted AI-conference papers

2026

4D Local Modeling Toward Dynamic Global Perception for Ambiguity-free Rotation-Invariant Point Cloud Analysis

Jiaxun Guo, Wentao Fan, Manar Amayri, Nizar Bouguila

CVPR 2026

Rotation invariance remains a core challenge in point cloud analysis, where existing methods often struggle with structural ambiguities and insufficient global context. Most rotation-invariant (RI) representations are derived from local coordinate systems, which inherently suffer from point-pair amb

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2026

Enhancing Rotation-Invariant 3D Learning with Global Pose Awareness and Attention Mechanisms

Jiaxun Guo, Manar Amayri, Nizar Bouguila, Xin Liu, Wentao Fan

AAAI 2026technical

Recent advances in rotation-invariant (RI) learning for 3D point clouds typically replace raw coordinates with handcrafted RI features to ensure robustness under arbitrary rotations. However, these approaches often suffer from the loss of global pose information, making them incapable of distinguish

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