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arXiv · 2507.00984

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

Xihang Yu·Rajat Talak·Jingnan Shi·Ulrich Viereck·Igor Gilitschenski·Luca Carlone
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Abstract

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.

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Xihang Yu, Rajat Talak, Jingnan Shi, Ulrich Viereck, Igor Gilitschenski, Luca Carlone. 2025-07-01. Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation. https://arxiv.org/abs/2507.00984

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