360MonoDepth: High-Resolution 360° Monocular Depth Estimation (2111.15669v2)

Abstract: 360{\deg} cameras can capture complete environments in a single shot, which makes 360{\deg} imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360{\deg} data, particularly for high resolutions like 2K (2048x1024) and beyond that are important for novel-view synthesis and virtual reality applications. Current CNN-based methods do not support such high resolutions due to limited GPU memory. In this work, we propose a flexible framework for monocular depth estimation from high-resolution 360{\deg} images using tangent images. We project the 360{\deg} input image onto a set of tangent planes that produce perspective views, which are suitable for the latest, most accurate state-of-the-art perspective monocular depth estimators. To achieve globally consistent disparity estimates, we recombine the individual depth estimates using deformable multi-scale alignment followed by gradient-domain blending. The result is a dense, high-resolution 360{\deg} depth map with a high level of detail, also for outdoor scenes which are not supported by existing methods. Our source code and data are available at https://manurare.github.io/360monodepth/.

• The paper presents a tangent image framework that projects 360° inputs into perspective views to enable state-of-the-art monocular depth estimation.
• The approach achieves enhanced depth accuracy, as demonstrated by low RMSE and MAE on high-resolution datasets like Matterport3D and Replica.
• The paper establishes a scalable solution that paves the way for advanced VR, robotics, and panoramic imaging applications.

Comprehensive Overview of 360MonoDepth: High-Resolution 360° Monocular Depth Estimation

The paper "360MonoDepth: High-Resolution 360° Monocular Depth Estimation" by Manuel Rey-Area et al. presents a novel framework designed to extend the capabilities of monocular depth estimation to 360-degree high-resolution images, effectively addressing challenges inherent in existing methods. Their approach leverages advances in convolutional neural networks (CNNs) and creatively applies them to 360° imagery, which is gaining traction in applications such as virtual reality (VR) and omnidirectional scene understanding.

Technical Contributions

The authors introduce a methodology that decomposes 360° images into perspective tangent images, facilitating the application of state-of-the-art monocular depth estimation techniques typically reserved for perspective images. This is accomplished by projecting the spherical input image onto tangent views and calculating depth estimates for each using robust CNN models like MiDaS. The individual depth maps are then realigned and merged using deformable alignment fields and gradient-domain blending, resulting in a cohesive high-resolution 360° disparity map.

Key technical contributions of the paper include:

1. Tangent Image Framework: The paper presents a simplified yet efficient framework for generating perspective tangent images from 360° inputs. This establishes a mechanism for monocular depth estimation at high resolutions, critical for applications demanding greater visual fidelity.
2. Scalable Solution: By supporting high-resolution outputs using tangent image methodologies, the work ensures compatibility with future advancements in monocular depth estimation, potentially enriching VR experiences.
3. Ground-truth Depth Maps: The authors contribute valuable datasets by creating ground-truth depth maps aligned with Matterport3D's stitched skyboxes, fostering future research in high-resolution 360° depth estimation.

Numerical Results and Analysis

Evaluated on datasets such as Matterport3D and Replica, the proposed framework demonstrates significantly enhanced performance compared to methods like OmniDepth and UniFuse. The reported results indicate superior fidelity in depth retrieval demonstrated by metrics like RMSE and MAE, particularly on test sets not used for training, thus showcasing the model's generalization prowess. For example, the framework achieves top performance metrics on images at 4096×2048 resolution, further validating its ability to work efficiently with high-resolution data.

Implications and Future Directions

This work carries significant implications for the deployment of 360° imagery in immersive applications like VR, where depth perception can dramatically improve user immersion. By overcoming resolution constraints and enhancing depth granularity, the methodology opens avenues for more interactive and realistic virtual environments.

The applicability of this framework could extend to areas such as autonomous navigation in robotics, augmented reality content creation, and panoramic imaging systems, wherein depth cues are crucial for object interaction and environmental mapping.

The future trajectory of this research could involve further optimization of alignment and blending techniques to reduce computational load and improve real-time processing capability. Additionally, integrating this methodology with evolving models using transformers for depth prediction could offer finer granularity and potentially mitigate challenges associated with complex outdoor environments.

In conclusion, the paper presents a forward-thinking approach to monocular depth estimation in 360° spaces, setting a foundation for subsequent research and technological growth in multiple fields reliant on precise depth information.