FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects (2310.12974v1)

Abstract: In this work, we address the challenging task of 3D object recognition without the reliance on real-world 3D labeled data. Our goal is to predict the 3D shape, size, and 6D pose of objects within a single RGB-D image, operating at the category level and eliminating the need for CAD models during inference. While existing self-supervised methods have made strides in this field, they often suffer from inefficiencies arising from non-end-to-end processing, reliance on separate models for different object categories, and slow surface extraction during the training of implicit reconstruction models; thus hindering both the speed and real-world applicability of the 3D recognition process. Our proposed method leverages a multi-stage training pipeline, designed to efficiently transfer synthetic performance to the real-world domain. This approach is achieved through a combination of 2D and 3D supervised losses during the synthetic domain training, followed by the incorporation of 2D supervised and 3D self-supervised losses on real-world data in two additional learning stages. By adopting this comprehensive strategy, our method successfully overcomes the aforementioned limitations and outperforms existing self-supervised 6D pose and size estimation baselines on the NOCS test-set with a 16.4% absolute improvement in mAP for 6D pose estimation while running in near real-time at 5 Hz.

An Analysis of FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects

The paper entitled "FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects" presents a self-supervised approach that addresses the comprehensive challenge of 3D object recognition and localization from a single RGB-D image input without the necessity of real-world 3D labeled data. This research is situated at the intersection of computer vision and robotics, with implications for fields such as autonomous navigation and robotic manipulation.

Overview and Methodology

The authors propose an innovative framework, FSD, designed for fast and effective categorical 6D pose and size estimation combined with shape reconstruction tasks. The primary contribution of this work is its ability to operate without real-world 3D labels such as meshes or 6D pose annotations, thereby negating the need for inference time optimization. The methodology involves using a multi-stage training pipeline that first utilizes synthetic data with both 2D and 3D supervised losses and subsequently transfers performance to real-world domains through additional stages guided by 2D supervision and 3D self-supervised losses.

Each stage of the pipeline plays a critical role:

1. Synthetic Pre-training Stage: This stage is crucial for learning 3D priors from fully labeled synthetic data. The use of CAMERA dataset facilitates a comprehensive initial learning process with both 2D and 3D labels.
2. Mixed Training Stage: Here the authors introduce a blend of synthetic and real datasets, allowing the model to preserve its 3D priors while beginning to adapt to real-world data characteristics. This intermediate step is vital for mitigating catastrophic forgetting.
3. Fine-tuning Stage: At this stage, the model is exclusively fine-tuned on real-world data to further refine its understanding and adaptability to real-world scenarios.

Numerical Results

The paper provides quantitative evaluations on standard benchmarks, demonstrating FSD's superiority over several state-of-the-art methods, including both fully supervised models and those relying on self-supervision. Specifically, the model achieves a notable 16.4% improvement in mean Average Precision (mAP) for 6D pose estimation on the NOCS dataset when compared to existing self-supervised methods. This significant performance enhancement indicates FSD's robust applicability in real-world settings despite using no 3D labeled data at the training stage.

The authors also benchmark the inference speed against previous works, showing that their method operates in near real-time at 5 Hz. This performance boost is pivotal for potential real-world applications where rapid inference is necessary.

Implications and Future Directions

The implications of deploying a unified model capable of categorical predictions across multiple object classes without category-specific adjustments are profound. Such flexibility suggests possible applications in environments where computing resources or labeled datasets are limited or difficult to obtain.

Future developments inspired by this work could explore more complex environments and the integration of more sophisticated self-supervised techniques to further enhance performance and generalization capabilities. Additionally, extending this research to include dynamic scenes or interaction-centric tasks could open new avenues in robotics and autonomous systems.

Conclusion

FSD offers a significant step forward in the application of self-supervised learning to 3D object recognition and pose estimation. By eliminating the reliance on real-world 3D labels while maintaining high accuracy and efficiency, this framework promises to substantially reduce the cost and labor associated with model training in real-world applications. The insights from this research could catalyze future work in scalable, efficient 3D perception in diverse environmental contexts.