Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 26 references that are in the map. First posted on arXiv 1 Jul 2025.
Builds on
23 of 26 references.
References · 23
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11 more
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Citing papers per year: 2026: 2.
2 papers.