提出单步多视角自适应方法，显著提升3D点云模型推理速度与鲁棒性。
MAMVI: 3D Test-Time Adaptation via Masked Multi-View Point Clouds
- 用统一单步优化替代逐视图迭代，减少冗余计算
- 在ShapeNet-C和ScanObjectNN-C上达到最优准确率
- 推理速度提升4.9至8.9倍，适合实时场景
3D点云模型在传感器噪声、遮挡和环境变化导致的分布偏移下性能显著下降。测试时自适应（TTA）成为缓解此问题的实用范式。近期多视角增强被证明可提升3D TTA表现，但现有方法常受限于独立处理各视角的串行优化，导致重复优化步骤带来巨大推理延迟，难以满足实时需求。为此，本文提出掩码多视角测试时自适应（MAMVI），以统一单步适应替代串行优化。MAMVI采用混合掩码策略：固定比例保证稳定性，Beta分布采样增强多样性。通过聚合多视角损失，基于多视图共识实现单次反向传播的适应。此外，引入置信度自适应学习率，动态调节每样本适应强度。在ModelNet-40C、ShapeNet-C和ScanObjectNN-C上的大量实验表明，MAMVI在ShapeNet-C和ScanObjectNN-C上达到当前最优精度，同时在ModelNet-40C上保持竞争力，推理速度提升4.9至8.9倍，极具实时应用潜力。代码已开源。
原文摘要 · Abstract (English)
3D point cloud models suffer significant performance degradation under distribution shifts caused by sensor noise, occlusions, and environmental changes. Test-time adaptation (TTA) has emerged as a practical paradigm for mitigating this issue during inference. Recently, leveraging multi-view augmentation has shown promise in improving 3D TTA performance. However, existing multi-view approaches are often constrained by sequential optimization that treats each view independently. This sequential optimization leads to substantial inference latency due to repetitive optimization steps, making real-time adaptation impractical. To address this, we propose Masked Multi-View Test-Time Adaptation (MAMVI), which replaces sequential optimization with a unified single-step adaptation. Specifically, MAMVI utilizes a hybrid masking strategy that combines fixed ratios for stability with Beta-distributed sampling for diversity. By aggregating losses across multiple views, MAMVI performs adaptation through a single backward pass based on multi-view consensus. Additionally, a confidence-based adaptive learning rate is used to dynamically adjust the adaptation intensity for each sample. Extensive experiments on ModelNet-40C, ShapeNet-C, and ScanObjectNN-C demonstrate that MAMVI achieves state-of-the-art accuracy on ShapeNet-C and ScanObjectNN-C. Moreover, it remains competitive on ModelNet-40C while delivering 4.9-8.9 times faster inference, making it highly suitable for real-time applications. Our code is available at https://github.com/Inseok-kong/MAMVI
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