Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation
(2406.12849)Abstract
Accurately estimating depth in 360-degree imagery is crucial for virtual reality, autonomous navigation, and immersive media applications. Existing depth estimation methods designed for perspective-view imagery fail when applied to 360-degree images due to different camera projections and distortions, whereas 360-degree methods perform inferior due to the lack of labeled data pairs. We propose a new depth estimation framework that utilizes unlabeled 360-degree data effectively. Our approach uses state-of-the-art perspective depth estimation models as teacher models to generate pseudo labels through a six-face cube projection technique, enabling efficient labeling of depth in 360-degree images. This method leverages the increasing availability of large datasets. Our approach includes two main stages: offline mask generation for invalid regions and an online semi-supervised joint training regime. We tested our approach on benchmark datasets such as Matterport3D and Stanford2D3D, showing significant improvements in depth estimation accuracy, particularly in zero-shot scenarios. Our proposed training pipeline can enhance any 360 monocular depth estimator and demonstrates effective knowledge transfer across different camera projections and data types. See our project page for results: https://albert100121.github.io/Depth-Anywhere/
Overview
-
The paper presents a novel depth estimation framework for 360-degree imagery by leveraging state-of-the-art perspective depth models to create pseudo depth labels and incorporating both labeled and pseudo-labeled data in a semi-supervised joint training regime.
-
Key contributions include the use of perspective model distillation, offline mask generation to identify invalid regions, and random rotation preprocessing to enhance training stability and accuracy.
-
Experimental results demonstrate significant improvements in depth estimation accuracy on benchmark datasets like Matterport3D and Stanford2D3D, showcasing the method's ability to generalize well in zero-shot scenarios.
Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation
The paper "Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation" addresses a critical challenge in the field of computer vision, specifically the accurate estimation of depth in 360-degree imagery. The importance of this task spans a variety of applications, including virtual reality, autonomous navigation, and immersive media. Previous methods developed for perspective-view images have shown limited efficacy when directly applied to 360-degree images, primarily due to differences in camera projection and inherent distortions. Furthermore, the lack of labeled 360-degree data exacerbates the problem, leading to subpar performance of existing 360-degree methods.
The authors propose a novel depth estimation framework leveraging state-of-the-art (SOTA) perspective depth estimation models to generate pseudo labels for 360-degree images. Their methodology can be described in two key stages: offline mask generation for identifying invalid regions, and an online semi-supervised joint training regime utilizing both labeled and pseudo-labeled data.
Key Contributions
Perspective Model Distillation: The approach uses SOTA perspective depth estimation models as teacher models, applying a six-face cube projection technique to generate pseudo depth labels for unlabeled 360-degree images. This ingenious application effectively harnesses the wealth of available perspective-view data and expands its utility to 360-degree depth estimation.
Offline Mask Generation: The authors employ detection and segmentation models to generate masks for invalid regions in the 360-degree data. This step is crucial for omitting areas such as the sky and watermarks during the depth estimation, thus stabilizing the training process.
Online Semi-Supervised Joint Training: The training regime involves using a mixed batch of labeled and pseudo-labeled data, with an equal distribution between the two. Incorporating pseudo labels generated by the teacher perspective model during training allows for robust depth estimation performance.
Random Rotation Preprocessing: To mitigate issues such as cube artifacts, which arise from separate estimation of each cube face, the authors introduce random rotation preprocessing. This step ensures a more comprehensive understanding of the scene by providing diverse perspectives.
Experimental Results
The method was validated on benchmark datasets such as Matterport3D and Stanford2D3D. The authors report significant improvements in depth estimation accuracy, particularly in zero-shot scenarios. The performance metrics include Absolute Mean Relative Error (AbsRel) and $\delta_j$ accuracy, which denote the proportion of accurate predictions within specified error bounds.
- Matterport3D Benchmark: The proposed method demonstrated superior performance with AbsRel reductions and improvements in $\delta_j$ accuracy metrics, outperforming existing 360-degree depth models such as UniFuse and BiFuse++.
- Zero-shot Evaluation on Stanford2D3D: The approach showed enhanced generalization capabilities, with marked improvements in zero-shot settings, where models trained on one dataset were evaluated on another. This capability is essential for the practical deployment of these models in varied real-world applications.
Implications and Future Directions
The paper's contributions extend beyond immediate numerical improvements. The method sets a precedent for the effective use of perspective-view models to tackle 360-degree image tasks. This opens several avenues for future research, including:
- Advanced Unlabeled Data Utilization: Exploring other sophisticated data augmentation and pseudo-labeling techniques to further improve the robustness and accuracy of 360-degree depth estimation models.
- Cross-Domain Adaptation: Investigating the adaptability of the proposed method across different datasets, camera models, and projection techniques to enhance the generalization and deployment capabilities.
- Integration with Real-time Systems: Applying the framework to real-time processing systems beneficial for applications in virtual reality and autonomous driving, where rapid and reliable depth estimation is critical.
In conclusion, this paper provides a substantial contribution to the field of 360-degree depth estimation by leveraging the resources and advancements made in perspective-view depth estimation. The methodology's robustness, validated through rigorous experiments, demonstrates its potential as a foundational approach for future research and development in this domain.
Create an account to read this summary for free: