PCoTTA：面向多任务点云理解的持续测试时自适应
PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding
AI总结：
该研究提出面向多任务点云理解的持续测试时自适应框架PCoTTA，通过自动原型混合、高斯泼溅特征偏移、对比原型排斥三个组件缓解遗忘与误差累积，提升模型对持续变化目标域的迁移能力，实验验证了其优越性并建立新基准。
摘要：
本文提出PCoTTA，这是一种创新前沿的多任务点云理解持续测试时自适应（Continual Test-Time Adaptation, CoTTA）框架，可提升模型对持续变化目标域的迁移能力。我们为PCoTTA设计了实用且符合现实的多任务设置，在持续自适应过程中用统一模型处理多项任务。PCoTTA包含三个核心组件：自动原型混合（automatic prototype mixture, APM）、高斯泼溅特征偏移（Gaussian Splatted feature shifting, GSFS）以及对比原型排斥（contrastive prototype repulsion, CPR）。首先，APM通过相似度平衡因子自动混合源原型与可学习原型，避免灾难性遗忘。其次，GSFS将测试样本动态向源域偏移，以在线方式缓解误差累积。此外，CPR将最近的可学习原型拉近至测试特征，同时将其与其他原型推远，使各原型在自适应过程中具备可区分性。实验对比构建了新基准，证明PCoTTA在提升模型对持续变化目标域的迁移能力方面具有优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
