Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion (2312.12471v1)

Abstract: Monocular depth estimation has experienced significant progress on terrestrial images in recent years, largely due to deep learning advancements. However, it remains inadequate for underwater scenes, primarily because of data scarcity. Given the inherent challenges of light attenuation and backscattering in water, acquiring clear underwater images or precise depth information is notably difficult and costly. Consequently, learning-based approaches often rely on synthetic data or turn to unsupervised or self-supervised methods to mitigate this lack of data. Nonetheless, the performance of these methods is often constrained by the domain gap and looser constraints. In this paper, we propose a novel pipeline for generating photorealistic underwater images using accurate terrestrial depth data. This approach facilitates the training of supervised models for underwater depth estimation, effectively reducing the performance disparity between terrestrial and underwater environments. Contrary to prior synthetic datasets that merely apply style transfer to terrestrial images without altering the scene content, our approach uniquely creates vibrant, non-existent underwater scenes by leveraging terrestrial depth data through the innovative Stable Diffusion model. Specifically, we introduce a unique Depth2Underwater ControlNet, trained on specially prepared {Underwater, Depth, Text} data triplets, for this generation task. Our newly developed dataset enables terrestrial depth estimation models to achieve considerable improvements, both quantitatively and qualitatively, on unseen underwater images, surpassing their terrestrial pre-trained counterparts. Moreover, the enhanced depth accuracy for underwater scenes also aids underwater image restoration techniques that rely on depth maps, further demonstrating our dataset's utility. The dataset will be available at https://github.com/zkawfanx/Atlantis.

• The paper proposes a novel pipeline that leverages Stable Diffusion and ControlNet to synthesize underwater scenes with accurate depth maps, effectively bridging domain gaps.
• It shows significant improvements in depth estimation performance by reducing RMSE and absolute relative errors compared to traditional methods.
• The innovative dataset enhances underwater image enhancement tasks and offers scalable potential for training more reliable depth estimation models.

Essay on Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion

Recent efforts to address the challenges of underwater depth estimation highlight significant advancements, as delineated in the paper titled "Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion." This paper sets out to overcome the existing limitations posed by inadequate underwater depth estimation data. Its focus is on creating a robust dataset using cutting-edge techniques, notably leveraging Stable Diffusion and ControlNet, to generate synthetic underwater scenes with accurate depth maps. This approach marks a substantial improvement over previous efforts primarily constrained by domain gaps and limited realism in synthetic data.

The paper underscores the inadequacy of existing underwater datasets, which are either costly and limited in diversity or fail to bridge the domain gap between terrestrial and underwater environments. Traditional methods, such as style-transferred GAN-based models, often produce underwater images with notable domain discrepancies, affecting the depth estimation's reliability. By proposing a novel pipeline, the authors introduce a dynamic solution that generates photorealistic underwater imagery, effectively reducing this gap.

A pivotal element of this work is the introduction of the Depth2Underwater ControlNet, trained on carefully curated triplets of underwater images, depth maps, and descriptive captions. This novel architecture utilizes the robust capabilities of Stable Diffusion to synthesize lively and diverse underwater environments. Through this, the generated dataset possesses high domain relevance and visual fidelity, crucial for training supervised depth estimation models.

Quantitative evaluations on real underwater datasets further substantiate the proposed method's efficacy. Notable enhancements were observed in models traditionally trained on terrestrial datasets, such as KITTI and NYU Depthv2, when replaced with training from the Atlantis dataset. The improvement is evident across multiple performance metrics, including RMSE and absolute relative errors. Additionally, qualitative assessments reveal improved depth mapping in challenging underwater conditions.

Importantly, the utility of the depth models trained on Atlantis extends to underwater image enhancement applications. Using the Sea-thru algorithm, the paper illustrates significant visual enhancement in underwater images, a direct testament to the dataset's richness and applicability. This capability resonates well in practical scenarios, where reliable single-image depth estimation is indispensable.

For future developments, the dataset's scalability and alignment with terrestrial depth estimators suggest promising avenues. There lies potential in integrating this methodology with other emerging technologies to boost model performance further. Moreover, as AI continues to evolve, extending similar frameworks to other challenging environments could open new frontiers in model training through synthetic data generation.

In conclusion, "Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion" presents a formidable stride in correcting the disparities between terrestrial and underwater depth estimation models. Its methodological innovations not only enhance current underwater imaging techniques but also set a precedent for future research endeavors in synthetic dataset generation, promising more accurate depth modeling across different domains.