Title: Unsupervised Domain Adaptation for 6-DoF Pose Estimation with Contrastive Alignment and Pseudo-Label Refinement
Primary area: applications to robotics, autonomy, planning
Keywords: 6DoF pose estimation, keypoint regression, unsupervised domain adaptation, contrastive learning

Abstract: Recent advances in deep learning have enabled accurate 6-DoF pose estimation of known objects from monocular images, driving progress in computer vision and robotics. However, the lack of abundant annotated data in certain domains often necessitates training models on synthetic data, which frequently fail to generalize to real-world scenarios due to significant domain shifts. To address this challenge, unsupervised domain adaptation (UDA) techniques offer a promising solution by enabling models to leverage labeled synthetic data while adapting effectively to unlabeled real-world data. Many approaches rely solely on self-supervision in the target domain, which has inherent limitations, and some perform only global feature alignment, potentially aligning semantically irrelevant features while neglecting task-specific cues critical for accurate adaptation. This paper introduces CAPLR, a unified keypoint-based UDA framework for 6-DoF object pose estimation that bridges the synthetic to real domain gap through contrastive alignment and pseudo-label refinement. CAPLR comprises three key components: (1) Cross-Domain Pairing to enable structured knowledge transfer by identifying corresponding image pairs across domains; (2) Contrastive Alignment to perform patch-level feature alignment at both intermediate backbone and task-specific head layers; and (3) Consistency-Based Pseudo-Label Refinement to improve reliability by encouraging stable predictions across augmented views of the same image. Extensive experiments demonstrate that CAPLR achieves state-of-the-art performance across multiple well-known 6-DoF pose estimation benchmarks featuring diverse and challenging scenarios.

TL;DR: A unified keypoint-based UDA framework for 6-DoF object pose estimation that bridges the synthetic-to-real domain gap via Contrastive Alignment and Pseudo-Label Refinement.