标题： 箱体姿态和形状估计以及大规模仓库自动化的领域自适应 显示英文标题

标题： Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation

摘要： 现代仓库自动化系统依赖于生成大量数据的智能机器人车队——其中大部分数据未被标注。 本文开发了一种自监督领域适应流程，利用真实世界中的未标注数据来提高感知模型，而无需手动标注。 我们的工作特别关注于估计箱子的姿态和形状，并提出了一个正确并认证的自监督箱子姿态和形状估计流程。 我们在一系列模拟和实际工业环境中对我们的方法进行了广泛评估，包括适应一个包含50,000张图像的大规模真实世界数据集。 自监督模型显著优于仅在模拟中训练的模型，并且在零样本3D边界框估计基线上表现出显著改进。 显示英文摘要

摘要： Modern warehouse automation systems rely on fleets of intelligent robots that generate vast amounts of data -- most of which remains unannotated. This paper develops a self-supervised domain adaptation pipeline that leverages real-world, unlabeled data to improve perception models without requiring manual annotations. Our work focuses specifically on estimating the pose and shape of boxes and presents a correct-and-certify pipeline for self-supervised box pose and shape estimation. We extensively evaluate our approach across a range of simulated and real industrial settings, including adaptation to a large-scale real-world dataset of 50,000 images. The self-supervised model significantly outperforms models trained solely in simulation and shows substantial improvements over a zero-shot 3D bounding box estimation baseline.