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Linlian Jiang

Doctorat - Concordia
Superviseur⋅e principal⋅e
Yang Wang
Sujets de recherche
Adaptation en phase de test
Apprentissage automatique appliqué
Modèles génératifs
Robotique
Vision par ordinateur
Site web

Publications

PointMAC: Meta-Learned Adaptation for Robust Test-Time Point Cloud Completion
Linlian Jiang
Rui Ma
Li Gu
Ziqiang Wang
Xinxin Zuo
Yang Wang
Point cloud completion is essential for robust 3D perception in safety-critical applications such as robotics and augmented reality. However… (voir plus), existing models perform static inference and rely heavily on inductive biases learned during training, limiting their ability to adapt to novel structural patterns and sensor-induced distortions at test time. To address this limitation, we propose PointMAC, a meta-learned framework for robust test-time adaptation in point cloud completion. It enables sample-specific refinement without requiring additional supervision. Our method optimizes the completion model under two self-supervised auxiliary objectives that simulate structural and sensor-level incompleteness. A meta-auxiliary learning strategy based on Model-Agnostic Meta-Learning (MAML) ensures that adaptation driven by auxiliary objectives is consistently aligned with the primary completion task. During inference, we adapt the shared encoder on-the-fly by optimizing auxiliary losses, with the decoder kept fixed. To further stabilize adaptation, we introduce Adaptive
2025-12-02
Conference on Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net