BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation (2407.17952v2)

Abstract: By training over large-scale datasets, zero-shot monocular depth estimation (MDE) methods show robust performance in the wild but often suffer from insufficient detail. Although recent diffusion-based MDE approaches exhibit a superior ability to extract details, they struggle in geometrically complex scenes that challenge their geometry prior, trained on less diverse 3D data. To leverage the complementary merits of both worlds, we propose BetterDepth to achieve geometrically correct affine-invariant MDE while capturing fine details. Specifically, BetterDepth is a conditional diffusion-based refiner that takes the prediction from pre-trained MDE models as depth conditioning, in which the global depth layout is well-captured, and iteratively refines details based on the input image. For the training of such a refiner, we propose global pre-alignment and local patch masking methods to ensure BetterDepth remains faithful to the depth conditioning while learning to add fine-grained scene details. With efficient training on small-scale synthetic datasets, BetterDepth achieves state-of-the-art zero-shot MDE performance on diverse public datasets and on in-the-wild scenes. Moreover, BetterDepth can improve the performance of other MDE models in a plug-and-play manner without further re-training.

• The paper introduces the BetterDepth framework that integrates feed-forward and diffusion models for robust global and local depth estimation.
• It leverages global pre-alignment and local patch masking to accelerate training and enhance fine-grained detail in zero-shot scenarios.
• The plug-and-play design improves various pre-trained MDE models, achieving state-of-the-art results on benchmarks like NYUv2.

Insightful Overview of BetterDepth Paper

The paper "BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation" introduces a novel approach to improve zero-shot monocular depth estimation (MDE) using a conditional diffusion model termed BetterDepth. This method aims to leverage the complementary strengths of existing feed-forward MDE models and diffusion-based MDE approaches to achieve both robust affine-invariant performance and fine-grained detail capture.

Key Contributions

1. BetterDepth Framework: This framework incorporates a pre-trained feed-forward depth model (M_FFD) to ensure global depth estimation accuracy and employs a diffusion-based refiner (M_DM) for iterative detail enhancement. This design uniquely balances the need for precise global depth context and local detail refinement.
2. Training Strategies: The authors introduce global pre-alignment and local patch masking techniques to improve training efficacy. Global pre-alignment adjusts depth conditioning to more closely align with ground truth, enhancing its reliability. Local patch masking, on the other hand, filters out dissimilar patches to maintain strong conditioning while facilitating detailed refinement.
3. Plug-and-Play Capability: BetterDepth's architecture enables it to enhance various pre-trained MDE models without requiring additional training, showcasing remarkable flexibility and efficacy in practical applications.

Theoretical and Practical Implications

BetterDepth's methodology significantly impacts how monocular depth estimation can be achieved, particularly under zero-shot conditions. The paper's contributions can be viewed through several lenses:

1. Integration of Priors: By integrating geometric priors from pre-trained MDE models with the strong detail-refinement capabilities of diffusion models, BetterDepth provides a novel solution addressing the limitations inherent in both feed-forward and diffusion-based approaches.
2. Training Efficiency: The training procedure outlined in BetterDepth demonstrates faster convergence rates compared to traditional diffusion-based methods like Marigold. This efficiency is attributed to the effective use of pre-trained depth estimation as a conditioning mechanism, significantly reducing the need for extensive training on large datasets.
3. Inference Flexibility: The plug-and-play nature of BetterDepth allows it to be easily adapted to improve newer or previously unseen MDE models. This adaptability is particularly valuable as the field continues to evolve with the development of foundation models trained on vast datasets.

Numerical Results and Benchmarks

Quantitative evaluations against state-of-the-art MDE methods reveal that BetterDepth achieves superior performance on diverse benchmarks. Notably, BetterDepth-2K, even when trained with only 2,000 samples, outperforms many current methods across various metrics such as Absolute Relative difference (AbsRel) and accuracy at different thresholds (δ1). For instance, BetterDepth-2K records an AbsRel of 4.4% and δ1 of 97.9% on the NYUv2 dataset, highlighting its strong detail extraction and zero-shot generalizability.

Future Directions

The promising results of BetterDepth pave the way for several future research directions:

• Lightweight Model Integration: Future work could explore incorporating more efficient model architectures within BetterDepth to enhance deployment capabilities on resource-constrained devices.
• Scalability and Diverse Priors: Extending BetterDepth to handle diverse sources of priors beyond depth models, such as multi-modal data inputs, could further enhance its adaptability and performance.
• Real-Time Applications: Improving the inference speed and stability of BetterDepth to cater to real-time applications in fields like autonomous driving and robotics remains a compelling area for future exploration.

Conclusion

BetterDepth proposes a robust, flexible, and efficient solution for zero-shot monocular depth estimation. By effectively combining the strengths of feed-forward and diffusion-based approaches, it sets a new benchmark in the field, enabling models to achieve state-of-the-art performance with minimal training data and without compromising on detail precision. This work not only addresses current challenges but also opens new avenues for future research and practical applications in MDE.