Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning (2411.19458v2)

Abstract: Vision foundation models, particularly the ViT family, have revolutionized image understanding by providing rich semantic features. However, despite their success in 2D comprehension, their abilities on grasping 3D spatial relationships are still unclear. In this work, we evaluate and enhance the 3D awareness of ViT-based models. We begin by systematically assessing their ability to learn 3D equivariant features, specifically examining the consistency of semantic embeddings across different viewpoints. Our findings indicate that improved 3D equivariance leads to better performance on various downstream tasks, including pose estimation, tracking, and semantic transfer. Building on this insight, we propose a simple yet effective finetuning strategy based on 3D correspondences, which significantly enhances the 3D correspondence understanding of existing vision models. Remarkably, finetuning on a single object for one iteration results in substantial gains. Our code is available at https://github.com/qq456cvb/3DCorrEnhance.

• The paper introduces a novel fine-tuning strategy that leverages multiview equivariance to enhance 3D correspondence with minimal data.
• It systematically evaluates vision transformers on datasets like Objaverse and MVImgNet, linking improved view consistency to better performance.
• Enhanced equivariance leads to significant gains in pose estimation, video tracking, and semantic correspondence, advancing practical 3D understanding.

Overview of "Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning"

The paper under discussion presents an exploration into improving 3D correspondence understanding in vision transformers (ViTs) by enhancing their multiview equivariance. The authors focus on the ability of ViTs, a family of vision foundation models, to generate consistent semantic embeddings across diverse viewpoints. The paper introduces a fine-tuning method that significantly enhances 3D correspondence understanding, even when training on limited data such as a single object.

Key Contributions

1. Systematic Evaluation of 3D Equivariance in 2D Vision Models: The authors conduct an exhaustive assessment of ViTs for their capacity to capture 3D structures through multiview equivariance. This involves evaluating the consistency of feature generation across different views in datasets like Objaverse and MVImgNet.
2. Correlation Between 3D Equivariance and Downstream Task Performance: The research establishes a direct correlation between the quality of 3D equivariance and performance in tasks requiring 3D correspondence understanding. Specifically, better equivariance yields improved results in pose estimation, video tracking, and semantic correspondence. For instance, DINOv2, a vision transformer, demonstrates a strong correlation and achieves superior performance metrics on these tasks compared to its peers.
3. Proposed Fine-tuning Strategy: A novel and simple fine-tuning approach is developed, focusing on enhancing view equivariance in existing vision models. Remarkably, this strategy leads to substantial performance improvements across 3D tasks with minimal computational requirements. The method involves leveraging SmoothAP loss during training to enforce feature consistency between views, utilizing a small subset of synthetic data.

Quantitative and Qualitative Results

The paper provides compelling numerical results illustrating the impact of their fine-tuning method. For instance, on Objaverse and MVImgNet, the authors report improved Average Pixel Error (APE) and Percentage of Correct Dense Points (PCDP) post fine-tuning. DINOv2's performance on key metrics, such as a 9.58 improvement in pose estimation accuracy, highlights the effectiveness of their method. Additionally, qualitative results demonstrate enhanced visual consistency and stability in feature maps, further underpinning their claims.

Implications and Future Perspectives

The implications of this research are significant for advancing the capabilities of 3D-aware vision models. The fine-tuning method could be a stepping stone for future developments in machine perception, facilitating more robust and accurate 3D scene understanding from 2D images. The approach's efficiency in needing minimal data and rapid convergence suggests potential applications in real-world scenarios where quick adaptation to new environments or tasks is critical.

From a theoretical standpoint, the paper opens avenues for exploring the integration of multiview equivariance principles into broader vision model architectures, potentially improving their robustness and adaptability further. Future research could expand on these findings, exploring the generalizability of their approach across different domains and the integration with other model architectures.

In summary, this paper provides significant insights into enhancing 3D correspondence understanding through minimal feature finetuning, with strong implications for both practical applications and future theoretical advancements in AI-driven vision systems.