GenCOPE: ロボットピッキングのための合成から実への汎化カテゴリレベル物体姿勢推定
GenCOPE: Syn2Real Generalized Category-Level Object Pose Estimation for Robotic Picking
合成データのみで学習し実世界に直接汎化するカテゴリレベル物体姿勢推定手法を提案。2D/3D意味的一貫性制約と2D-3D相互融合によりドメインギャップを克服し、軽量なアーキテクチャでロボット操作に応用可能。
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著者: Jian Liu, Wei Sun, Zhenqi Dai, Hui Yang, Jian Xiao, Nicu Sebe, Na Zhao
分類: cs.CV
原文アブストラクト
Category-level object pose estimation (COPE), capable of generalizing to intra-class unknown objects, has become a core technique for robotic 3D scene understanding. However, existing COPE methods still require labor-intensive recollection of real-world training data for novel object categories, which limits their scalability in practical applications. This paper aims to achieve synthetic-to-real (Syn2Real) generalized COPE, where a model is trained solely on rendered synthetic data and directly generalized to real-world deployments. The central challenge lies in the significant domain gap between synthetic and real-world data, particularly in texture appearance. To address this, we aim to enhance domain generalization by learning domain-invariant representations that capture semantic commonalities among objects within the same category. We introduce 2D and 3D semantic consistency constraints to reduce the sensitivity of feature encoders to domain-specific features. In addition, we propose an end-to-end pose regression framework that performs 2D-3D cross consistency learning, leveraging dense cross-modality fusion to further refine pose estimation. Since simplicity and effectiveness are essential for real-world robotic deployment, our model operates exclusively on global features, yielding a highly lightweight and efficient architecture. Extensive experiments on the REAL275 and Wild6D benchmarks, as well as real-world robotic manipulation scenes, show superior Syn2Real generalization performance of our paradigm. Code and demos are released at https://paperreview99.github.io/GenCOPE/.