Improving 2D Feature Representations by 3D-Aware Fine-Tuning (2407.20229v1)

Abstract: Current visual foundation models are trained purely on unstructured 2D data, limiting their understanding of 3D structure of objects and scenes. In this work, we show that fine-tuning on 3D-aware data improves the quality of emerging semantic features. We design a method to lift semantic 2D features into an efficient 3D Gaussian representation, which allows us to re-render them for arbitrary views. Using the rendered 3D-aware features, we design a fine-tuning strategy to transfer such 3D awareness into a 2D foundation model. We demonstrate that models fine-tuned in that way produce features that readily improve downstream task performance in semantic segmentation and depth estimation through simple linear probing. Notably, though fined-tuned on a single indoor dataset, the improvement is transferable to a variety of indoor datasets and out-of-domain datasets. We hope our study encourages the community to consider injecting 3D awareness when training 2D foundation models. Project page: https://ywyue.github.io/FiT3D.

• The paper introduces a novel 3D Gaussian representation that lifts 2D features into a coherent 3D structure.
• It proposes a two-stage fine-tuning strategy that efficiently integrates 3D contextual awareness into 2D visual models.
• The approach achieves measurable improvements in semantic segmentation and depth estimation across diverse datasets.

Improving 2D Feature Representations by 3D-Aware Fine-Tuning

The paper "Improving 2D Feature Representations by 3D-Aware Fine-Tuning" presents a novel methodology for enhancing the performance of 2D visual foundation models by incorporating 3D contextual awareness. Traditional visual models are predominantly trained on 2D datasets, limiting their capacity to fully grasp the 3D structures within scenes and objects. The authors propose a two-step approach to mitigate this limitation, emphasizing the role of 3D awareness in enhancing 2D foundational models and improving performance on downstream tasks like semantic segmentation and depth estimation.

Key Contributions

1. 3D Gaussian Representation: The paper introduces a technique to lift 2D semantic features into a 3D Gaussian representation. This approach allows these 2D features from multiple views to amalgamate into a coherent 3D structure, preserving multi-view consistency and leveraging 3D spatial information efficiently.
2. Two-Stage Fine-Tuning Strategy: The paper outlines a two-stage process to incorporate 3D awareness into 2D models:
   • Lifting Stage: 2D features are elevated to a 3D Gaussian representation, ensuring continuity in multi-view scenes.
   • Fine-Tuning Stage: The rendered 3D-aware features are utilized to fine-tune the 2D foundation models, enriching them with 3D contextual information. This process is efficient and does not require substantial computational resources.
3. Improved Downstream Task Performance: By integrating these refined features, the model demonstrates improved capabilities in downstream tasks. The paper reports marked improvements in semantic segmentation and depth estimation across various datasets, even in out-of-domain scenarios.

Methodological Insights

• Feature Representation: The method leverages recent advances in neural scene representations particularly focusing on Gaussian splatting, for rapid training and rendering. This choice provides an efficient and memory-conservative way to lift and manipulate 2D feature spaces into 3D, making it suitable for large-scale applications.
• Scalability and Transferability: Despite training on a single indoor dataset, the improvements are generalized across different data scenarios and domains. This highlights the potential for wide application without being constrained by specific datasets or environments.

Experimental Results

• Numerical Improvements: The paper provides strong quantitative results, showcasing enhancements like a 2.6% increase in mIoU for semantic segmentation on ScanNet++ and a reduction of 0.03 in RMSE for depth estimation, underscoring the practical benefits of the proposed methodology.
• Generalization Capabilities: The 3D-aware fine-tuning improves model performance not only in similar datasets but also in diverse ones, such as ADE20k and KITTI. This adaptability signifies the model's robustness in handling different scene types and conditions.

Implications and Future Directions

The integration of 3D features into 2D foundational models heralds significant potential in both theoretical and practical aspects. The findings suggest a new paradigm for representation learning, where models could evolve beyond traditional 2D limitations, enhancing their understanding and interpretation of spatial data.

Future work could explore the scalability of this approach across even broader and more varied datasets, potentially incorporating real-time applications, and investigating the implications of this fine-tuning process on other AI domains. Additionally, exploring integration with other types of 3D representations or combining with temporal data for video applications could further enhance model capabilities.

In conclusion, the paper presents a compelling argument for the incorporation of 3D awareness in 2D models, building a foundation for future research and development in more context-aware AI systems.