Zero-Shot Category-Level Object Pose Estimation (2204.03635v2)

Abstract: Object pose estimation is an important component of most vision pipelines for embodied agents, as well as in 3D vision more generally. In this paper we tackle the problem of estimating the pose of novel object categories in a zero-shot manner. This extends much of the existing literature by removing the need for pose-labelled datasets or category-specific CAD models for training or inference. Specifically, we make the following contributions. First, we formalise the zero-shot, category-level pose estimation problem and frame it in a way that is most applicable to real-world embodied agents. Secondly, we propose a novel method based on semantic correspondences from a self-supervised vision transformer to solve the pose estimation problem. We further re-purpose the recent CO3D dataset to present a controlled and realistic test setting. Finally, we demonstrate that all baselines for our proposed task perform poorly, and show that our method provides a six-fold improvement in average rotation accuracy at 30 degrees. Our code is available at https://github.com/applied-ai-lab/zero-shot-pose.

• The paper formalizes zero-shot category-level pose estimation by computing relative pose offsets without reliance on pose-labelled datasets or CAD models.
• The approach leverages self-supervised vision transformers to extract semantic features and uses cyclical distance with K-means clustering to establish robust correspondences.
• Empirical evaluations on the CO3D dataset demonstrate a six-fold improvement in pose accuracy, highlighting its potential for robotics and autonomous systems.

Zero-Shot Category-Level Object Pose Estimation

The paper "Zero-Shot Category-Level Object Pose Estimation" by Goodwin et al. addresses the challenge of estimating 6D object poses without requiring pose-labelled datasets or category-specific CAD models. This task is demanding, given both the semantic and geometric discrepancies object instances may exhibit within a category. The paper introduces a method leveraging semantic correspondences recovered from self-supervised vision transformers (ViTs) to perform zero-shot pose estimation, demonstrating significant improvements over baseline methods in the process.

Methodology and Contributions

The authors formalize the zero-shot pose estimation problem as one of calculating the relative pose offset between two instances of an object category. This problem mirrors real-world applications, where explicit pose annotations for novel objects are not available, yet embodied agents increasingly operate using visual inputs and depth sensors. The solution embodies a multi-step process: semantic correspondences are established between instances using features extracted from a ViT, optimal viewpoints are determined, and rigid body transformations are computed using depth information to complete the pose estimation.

The primary contributions are:

1. Formalizing the Problem: The paper delineates zero-shot category-level pose estimation as a real-world problem for embodied agents. It eliminates several assumptions inherent in previous work, such as access to pose-labelled datasets, CAD models, or limiting evaluations to object instances.
2. Novel Semantic Correspondence Method: A self-supervised ViT provides semantic feature understanding, which extends across novel instances and categories. A cyclical distance approach identifies semantic correspondences, filtered for spatial diversity via K-means clustering.
3. Empirical Evaluation: Using the CO3D dataset, which spans multiple categories with sufficient within-category diversity, the effectiveness of the zero-shot pose estimation method is rigorously evaluated under controlled conditions, confirming the six-fold improvement in pose accuracy over baselines.

Strong Numerical Results and Bold Claims

The paper showcases numerical superiority by achieving a six-fold increase in accuracy under certain conditions over traditional baselines, including ICP and PoseContrast. On average, the method reports a substantial reduction in median rotation error for predicting pose across twenty diverse categories. This demonstrates the robustness and adaptability of the proposed method under varied types of intra-category object variation.

Implications and Future Directions

Practically, this work facilitates advances in robotics and autonomous systems, aiding tasks like object manipulation, navigation, and interaction, where understanding spatial relations without detailed pose annotations is critical. While immediate applications may be found in logistics and domestic robots, theoretical implications further extend to enhancing unsupervised learning paradigms and improving generalizable representation learning.

Future research might investigate refining semantic correspondence strategies or combining semantic insights with physical interaction data for improving pose estimation under occlusions or partial visibility. Additionally, exploring how self-supervised features change when similar systems are trained on alternative datasets could uncover further insights into cross-domain robustness and feature generalization, which are essential as AI systems continue to expand into diverse and dynamic environments.

In conclusion, Goodwin et al. present a robust and innovative approach to zero-shot category-level object pose estimation, offering significant advancements over present methods in the absence of labeled pose datasets and category-specific CAD models. This work sets a foundation for continued exploration of semantic understanding in AI, pointing towards greater flexibility and capability in embodied agents tasked with interpreting the 3D world.