NVlabs/FoundationPose

This is the official implementation of our paper to be appeared in CVPR 2024 (Highlight)

Contributors: Bowen Wen, Wei Yang, Jan Kautz, Stan Birchfield

We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups. Our approach can be instantly applied at test-time to a novel object without fine-tuning, as long as its CAD model is given, or a small number of reference images are captured. We bridge the gap between these two setups with a neural implicit representation that allows for effective novel view synthesis, keeping the downstream pose estimation modules invariant under the same unified framework. Strong generalizability is achieved via large-scale synthetic training, aided by a large language model (LLM), a novel transformer-based architecture, and contrastive learning formulation. Extensive evaluation on multiple public datasets involving challenging scenarios and objects indicate our unified approach outperforms existing methods specialized for each task by a large margin. In addition, it even achieves comparable results to instance-level methods despite the reduced assumptions.

🤖 For ROS version, please check Isaac ROS Pose Estimation, which enjoys TRT fast inference and C++ speed up.

🥇 No. 1 on the world-wide BOP leaderboard (as of 2024/03) for model-based novel object pose estimation.