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FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
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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. Project page: https://nvlabs.github.io/FoundationPose/
Forward citations
Cited by 6 Pith papers
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Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation
SIDO morphs static demonstrations into counterfactual future-pose samples, training a goal-conditioned policy that, paired with a pose predictor, grasps objects whose motion was unseen during training.
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Generative Visual Foresight Meets Task-Agnostic Pose Estimation in Robotic Table-Top Manipulation
GVF-TAPE predicts future RGB-D frames from an image and text, then extracts end-effector poses to control a robot, achieving strong success rates without action-labeled data.
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GoTrack: Generic 6DoF Object Pose Refinement and Tracking
GoTrack uses optical flow between a synthetic object render and the input image to refine and track 6D poses of unseen objects, improving accuracy and speed over prior methods.
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You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping
A unified single-stage network jointly predicting semantic labels, centroid offsets, and Normalized Part Coordinate Space maps estimates category-level 6D part poses and sizes for articulated objects in real time.
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A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards
IKER uses VLM-generated keypoint rewards to train manipulation policies in simulation that transfer to a real robot, enabling multi-step tasks and replanning.
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Kitchen Robotic Manipulation utilizing Foundation Models
A modular perception pipeline using off-the-shelf foundation models achieves 89.12% ADI on a custom kitchen dishware dataset and performs real robot sink-to-dishwasher and cup-stacking tasks without retraining.
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