Any6D: Model-free 6D Pose Estimation of Novel Objects
Authors: name: Taeyeop Lee, name: Bowen Wen, name: Minjun Kang, name: Gyuree Kang, name: In So Kweon, name: Kuk-Jin Yoon

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