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:this https URL
View on arXiv@article{lee2025_2503.18673,
title={ Any6D: Model-free 6D Pose Estimation of Novel Objects },
author={ Taeyeop Lee and Bowen Wen and Minjun Kang and Gyuree Kang and In So Kweon and Kuk-Jin Yoon },
journal={arXiv preprint arXiv:2503.18673},
year={ 2025 }
}