title: Any6D: Model-free 6D Pose Estimation of Novel Objects
summary: 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
paper_authors._id: 67e34bdb8aa257f5a3ebe346
paper_authors.name: Taeyeop Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-26T20:45:09.497Z
paper_authors._id: 648283e7eb4befee37855c93
paper_authors.avatarUrl: /avatars/9e83accbd2410b0bcb5332df2b7f3ced.svg
paper_authors.fullname: Taeyeop Lee
paper_authors.type: user
paper_authors.user: taeyeop
paper_authors._id: 67e34bdb8aa257f5a3ebe347
paper_authors.name: Bowen Wen
paper_authors._id: 67e34bdb8aa257f5a3ebe348
paper_authors.name: Minjun Kang
paper_authors._id: 67e34bdb8aa257f5a3ebe349
paper_authors.name: Gyuree Kang
paper_authors._id: 67e34bdb8aa257f5a3ebe34a
paper_authors.name: In So Kweon
paper_authors._id: 67e34bdb8aa257f5a3ebe34b
paper_authors.name: Kuk-Jin Yoon
paper_title: Any6D: Model-free 6D Pose Estimation of Novel Objects
paper_summary: 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
paper_ai_summary: Any6D estimates the 6D pose and size of unknown objects using a single RGB-D image through a model-free framework, enhancing 2D-3D alignment and scale estimation across challenging conditions.
paper_ai_keywords: 6D object pose estimation RGB-D anchor image 2D-3D alignment metric scale estimation render-and-compare strategy pose hypotheses occlusions non-overlapping views diverse lighting conditions cross-environment variations REAL275 Toyota-Light HO3D YCBINEOAT LM-O