让点云模型持续适应变化环境，多任务下表现更稳定。
PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding
- 自动混合源与可学习原型，防止遗忘。
- 动态调整测试样本特征，减少误差累积。
- 原型区分机制提升多任务适应能力。
本文提出PCoTTA，一种面向多任务点云理解的持续测试时自适应框架，提升模型在不断变化目标域中的泛化能力。该框架在统一模型中处理多个任务，具备实际应用价值。包含三个核心组件：自动原型混合（APM）通过相似性平衡因子融合源原型与可学习原型，避免灾难性遗忘；高斯点状特征偏移（GSFS）动态将测试样本向源域特征空间移动，实现在线误差抑制；对比原型排斥（CPR）促使最近的可学习原型靠近测试特征，同时远离其他原型，增强原型可区分性。实验建立新基准，验证了PCoTTA在持续适应场景下的优越性能。
原文摘要 · Abstract (English)
In this paper, we present PCoTTA, an innovative, pioneering framework for Continual Test-Time Adaptation (CoTTA) in multi-task point cloud understanding, enhancing the model's transferability towards the continually changing target domain. We introduce a multi-task setting for PCoTTA, which is practical and realistic, handling multiple tasks within one unified model during the continual adaptation. Our PCoTTA involves three key components: automatic prototype mixture (APM), Gaussian Splatted feature shifting (GSFS), and contrastive prototype repulsion (CPR). Firstly, APM is designed to automatically mix the source prototypes with the learnable prototypes with a similarity balancing factor, avoiding catastrophic forgetting. Then, GSFS dynamically shifts the testing sample toward the source domain, mitigating error accumulation in an online manner. In addition, CPR is proposed to pull the nearest learnable prototype close to the testing feature and push it away from other prototypes, making each prototype distinguishable during the adaptation. Experimental comparisons lead to a new benchmark, demonstrating PCoTTA's superiority in boosting the model's transferability towards the continually changing target domain.
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