Any6D: Model-free 6D Pose Estimation of Novel Objects

• URL: http://arxiv.org/abs/2503.18673v2
• Date: Tue, 25 Mar 2025 06:18:47 GMT
• Title: Any6D: Model-free 6D Pose Estimation of Novel Objects
• Authors: Taeyeop Lee, Bowen Wen, Minjun Kang, Gyuree Kang, In So Kweon, Kuk-Jin Yoon,
• Abstract summary: We introduce Any6D, a model-free framework for 6D object pose estimation.<n>It requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes.<n>We evaluate our method on five challenging datasets.
• Score: 76.30057578269668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Any6D, a model-free framework for 6D object pose estimation that requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes. Unlike existing methods that rely on textured 3D models or multiple viewpoints, Any6D leverages a joint object alignment process to enhance 2D-3D alignment and metric scale estimation for improved pose accuracy. Our approach integrates a render-and-compare strategy to generate and refine pose hypotheses, enabling robust performance in scenarios with occlusions, non-overlapping views, diverse lighting conditions, and large cross-environment variations. We evaluate our method on five challenging datasets: REAL275, Toyota-Light, HO3D, YCBINEOAT, and LM-O, demonstrating its effectiveness in significantly outperforming state-of-the-art methods for novel object pose estimation. Project page: https://taeyeop.com/any6d

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