Any6D: Model-free 6D Pose Estimation of Novel Objects
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
Community
๐๐ผ๐ฑ๐ฒ: https://github.com/taeyeopl/Any6D
๐ช๐ฒ๐ฏ๐ฝ๐ฎ๐ด๐ฒ: https://taeyeop.com/any6d
๐ฃ๐ฎ๐ฝ๐ฒ๐ฟ: https://arxiv.org/pdf/2503.18673
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Novel Object 6D Pose Estimation with a Single Reference View (2025)
- AxisPose: Model-Free Matching-Free Single-Shot 6D Object Pose Estimation via Axis Generation (2025)
- HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimation (2025)
- Structure-Aware Correspondence Learning for Relative Pose Estimation (2025)
- Co-op: Correspondence-based Novel Object Pose Estimation (2025)
- SplatPose: Geometry-Aware 6-DoF Pose Estimation from Single RGB Image via 3D Gaussian Splatting (2025)
- FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views (2025)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper