Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator (2502.19204v2)

Abstract: Recent advances in zero-shot monocular depth estimation(MDE) have significantly improved generalization by unifying depth distributions through normalized depth representations and by leveraging large-scale unlabeled data via pseudo-label distillation. However, existing methods that rely on global depth normalization treat all depth values equally, which can amplify noise in pseudo-labels and reduce distillation effectiveness. In this paper, we present a systematic analysis of depth normalization strategies in the context of pseudo-label distillation. Our study shows that, under recent distillation paradigms (e.g., shared-context distillation), normalization is not always necessary, as omitting it can help mitigate the impact of noisy supervision. Furthermore, rather than focusing solely on how depth information is represented, we propose Cross-Context Distillation, which integrates both global and local depth cues to enhance pseudo-label quality. We also introduce an assistant-guided distillation strategy that incorporates complementary depth priors from a diffusion-based teacher model, enhancing supervision diversity and robustness. Extensive experiments on benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods, both quantitatively and qualitatively.

• The paper introduces a cross-context distillation framework that integrates local and global depth cues to improve depth pseudo-label quality for training.
• The authors propose a multi-teacher distillation approach leveraging diverse models like generative and encoder-decoder networks to enhance model robustness and accuracy.
• Empirical analysis highlights the benefits of local normalization strategies, showing the proposed methods achieve superior zero-shot performance on datasets like NYUv2, KITTI, ETH3D, ScanNet, and DIODE.

Distillation-Based Enhancements in Monocular Depth Estimation

The paper "Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator" explores the field of monocular depth estimation (MDE) from a single RGB image, which plays a pivotal role in comprehending 3D scenes. The authors investigate the advancements that have been made in leveraging zero-shot MDE and propose novel methodologies centered around depth normalization and distillation strategies to enhance the accuracy of depth prediction models.

Core Contributions

The paper makes several significant contributions to the field:

1. Cross-Context Distillation Framework: The authors introduce a framework that integrates local and global depth cues in order to improve pseudo-label quality for training depth estimation models. By employing both shared-context distillation and local-global distillation strategies, the framework enhances the model's ability to perceive details and understand broader scene contexts simultaneously.
2. Multi-Teacher Distillation Approach: This approach leverages the complementary strengths of multiple depth estimation models. By randomly selecting teacher models during training, the student model is exposed to a diverse set of pseudo-labels, thereby improving its robustness and accuracy. The combination of generative models, with their fine-detail capabilities, and more efficient models like encoder-decoders is shown to be especially effective.
3. Empirical Insights into Normalization Strategies: The paper systematically examines different depth normalization strategies, including global, local, hybrid, and no normalization. The findings uncover that local normalization, or a combination of approaches, can mitigate the issues introduced by global normalization, such as noise amplification.
4. Superior Performance on Benchmark Datasets: The proposed methods outperform state-of-the-art models in zero-shot settings across multiple benchmark datasets, namely NYUv2, KITTI, ETH3D, ScanNet, and DIODE, both quantitatively and qualitatively.

Implications and Future Directions

The implication of utilizing cross-context and multi-teacher distillation frameworks is substantial. These strategies not only enhance generalization from training datasets but also improve the model's adaptability to unseen scenes, making them particularly useful in applications requiring robust depth estimation, such as autonomous driving and robotic navigation.

The strong numerical results demonstrated by the proposed methodologies suggest the potential for future research directions. This could include further exploration into the optimization of pseudo-label generation, the implementation of more sophisticated multi-teacher frameworks, as well as applying similar distillation strategies to other perception tasks beyond MDE.

For researchers in the field of AI and computer vision, the insights provided in this paper lay the foundation for advancing depth estimation through distillation techniques. By challenging the traditional approaches to model training and evaluation, this research encourages the development of models that are not only more accurate but also capable of outperforming individual teacher networks.

In summary, the paper provides valuable contributions through innovative methodologies and comprehensive analysis, significantly advancing the prospects of monocular depth estimation in challenging scene understanding scenarios.