FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects (2312.08344v2)

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 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 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/

• The paper introduces a unified framework that performs 6D pose estimation and tracking using both model-based and model-free methods.
• It employs a novel transformer-based architecture with contrastive learning and LLM integration, achieving superior performance across multiple datasets.
• The approach alleviates dependency on extensive fine-tuning, offering broad applications in robotics and mixed reality with efficient, versatile processing.

FoundationPose: Unified 6D Pose Estimation and Tracking

The paper introduces FoundationPose, a unified framework designed for 6D object pose estimation and tracking of novel objects. This model supports both model-based and model-free approaches, offering instant applicability to novel objects without requiring fine-tuning, provided a CAD model exists or a small set of reference images is available.

FoundationPose stands out due to its robust generalizability which stems from large-scale synthetic training, using a novel transformer-based architecture, contrastive learning, and the integration of a LLM. Evaluations on multiple datasets demonstrate its superior performance over existing methods tailored for specific tasks, and its results are on par with instance-level methods that require more restrictive assumptions.

Methodology

FoundationPose leverages both model-based and model-free strategies, integrating a neural implicit representation for efficient view synthesis when no CAD model is available. This approach unifies the downstream modules for pose estimation across different setups. By employing synthetic training augmented with an LLM and diversified texture augmentation, the model achieves strong generalizability. This is bolstered by a novel transformer-based architecture and a contrastive learning framework.

The system design facilitates high efficiency and smooth performance in tracking tasks, employing temporal cues for enhanced accuracy over video sequences. For novel view synthesis in the model-free setup, an object-centric neural field is utilized, bridging the gap between the setups.

Results

The paper provides compelling numerical results indicating that FoundationPose surpasses existing specialized methods across multiple public datasets. For both pose estimation and tracking, the proposed framework achieves a significant increase in performance metrics. Notably, it offers competitive results to instance-level trained methods without imposing as many constraints.

Implications and Future Work

The practical implications of FoundationPose are substantial as it addresses the limitations of conventional instance and category-level methods, enabling application to arbitrary novel objects—a significant step forward for robotic manipulation and mixed reality applications. Theoretically, the work reflects a shift towards more generalized and flexible models in AI, reducing dependency on extensive instance-specific training data.

Future developments may include the exploration of multi-object pose estimation and further enhancement of the model's ability to handle complex, real-world environments without additional computational cost. Integrating detection into the unified framework could also streamline processes and improve system scalability.

In conclusion, FoundationPose represents a significant development in the field of 6D pose estimation and tracking, presenting a versatile, efficient method applicable across diverse scenarios with reduced prerequisites. The framework not only reinforces the potential of synthetic training environments but also sets a foundation for future innovations in AI-based object manipulation and interaction.