Unsupervised Domain Adaptation for Sim-to-Real Object Pose Estimation with Contrastive Alignment and Pseudo-Label Refinement

• URL: http://arxiv.org/abs/2606.21287v1
• Date: Fri, 19 Jun 2026 10:11:17 GMT
• Title: Unsupervised Domain Adaptation for Sim-to-Real Object Pose Estimation with Contrastive Alignment and Pseudo-Label Refinement
• Abstract summary: Unsupervised domain adaptation (UDA) enables robust transfer of knowledge from simulated to real environments.<n>We introduce CAPLR that targets the adaptation of pose-sensitive features in localized regions.<n>We show that CAPLR achieves state-of-the-art performance across multiple well-known object pose estimation benchmarks.
• Score: 14.854437007037683
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Unsupervised domain adaptation (UDA) enables robust transfer of knowledge from simulated to real environments while exploiting a subset of unlabeled target data to improve real-world performance. Existing UDA methods for Object pose estimation often rely on global feature matching, multi-stage larger frameworks, or image translation pipelines, which tend to overlook the pose-specific information embedded in feature representations. To bridge this limitation, we introduce CAPLR that targets the adaptation of pose-sensitive features in localized regions, ensuring that domain alignment preserves the geometric cues essential for accurate pose estimation. CAPLR achieves UDA with three key components: (1) Efficient Cross-Domain Pairing strategy leveraging intermediate features to identify pose similar image pairs across domains without supervision; (2) Contrastive Alignment to perform feature alignment at localised regions in both intermediate and task-specific representations; and (3) Consistency-Based Pseudo-Label Refinement to improve reliability by encouraging stable target predictions. Extensive experiments demonstrate that CAPLR achieves state-of-the-art performance across multiple well-known object pose estimation benchmarks featuring diverse and challenging scenarios.

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