FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

Abstract

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 finetuning as long as its CAD model is given or a small number of reference images are captured. Thanks to the unified framework the downstream pose estimation modules are the same in both setups with a neural implicit representation used for efficient novel view synthesis when no CAD model is available. 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. Project page: https://nvlabs.github.io/FoundationPose/