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

• URL: http://arxiv.org/abs/2507.00984v1
• Date: Tue, 01 Jul 2025 17:36:09 GMT
• Title: Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation
• Abstract summary: This paper develops a self-supervised domain adaptation pipeline to improve perception models without requiring manual annotations.<n>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.<n>The self-supervised model significantly outperforms models trained solely in simulation and shows substantial improvements over a zero-shot 3D bounding box estimation baseline.
• Score: 23.91093031135749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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