实例引导的无监督领域适应用于机器人语义分割
Instance-Guided Unsupervised Domain Adaptation for Robotic Semantic Segmentation
AI总结：
本文提出了一种实例引导的无监督领域适应方法，通过生成多视图一致的伪标签并利用基础模型进行细化，提升机器人语义分割的适应性能。
摘要：
语义分割网络对于机器人的感知至关重要，但当部署环境的视觉分布与训练所用的源数据集不同之时，其性能会下降。无监督领域适应（UDA）通过在没有外部监督的情况下适应网络以适应机器人的目标环境来解决这一挑战，利用机器人在长期运行中可能自然收集的大量数据。在这种情况下，UDA方法可以利用环境地图中的多视图一致性来以无监督的方式微调模型，以缓解领域偏移。然而，这些方法仍然容易受到跨视图实例级不一致的影响。在本工作中，我们提出了一种方法，从体积3D地图开始，生成多视图一致的伪标签。然后，我们利用基础模型的零样本实例分割能力来细化这些标签，强制实例级一致性。经过细化的注解用作自监督微调的监督，使机器人能够在部署时适应其感知系统。在真实世界数据上的实验表明，我们的方法在基于多视图一致性的最新UDA基线基础上，始终提高了性能，而无需在目标领域中使用任何真实标签。
英文摘要：
Semantic segmentation networks, which are essential for robotic perception, often suffer from performance degradation when the visual distribution of the deployment environment differs from that of the source dataset on which they were trained. Unsupervised Domain Adaptation (UDA) addresses this challenge by adapting the network to the robot's target environment without external supervision, leveraging the large amounts of data a robot might naturally collect during long-term operation. In such settings, UDA methods can exploit multi-view consistency across the environment's map to fine-tune the model in an unsupervised fashion and mitigate domain shift. However, these approaches remain sensitive to cross-view instance-level inconsistencies. In this work, we propose a method that starts from a volumetric 3D map to generate multi-view consistent pseudo-labels. We then refine these labels using the zero-shot instance segmentation capabilities of a foundation model, enforcing instance-level coherence. The refined annotations serve as supervision for self-supervised fine-tuning, enabling the robot to adapt its perception system at deployment time. Experiments on real-world data demonstrate that our approach consistently improves performance over state-of-the-art UDA baselines based on multi-view consistency, without requiring any ground-truth labels in the 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。
