Unsupervised Adversarial Depth Estimation using Cycled Generative Networks (1807.10915v1)

Abstract: While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during training. To cope with this issue, in this paper we present a novel unsupervised deep learning approach for predicting depth maps and show that the depth estimation task can be effectively tackled within an adversarial learning framework. Specifically, we propose a deep generative network that learns to predict the correspondence field i.e. the disparity map between two image views in a calibrated stereo camera setting. The proposed architecture consists of two generative sub-networks jointly trained with adversarial learning for reconstructing the disparity map and organized in a cycle such as to provide mutual constraints and supervision to each other. Extensive experiments on the publicly available datasets KITTI and Cityscapes demonstrate the effectiveness of the proposed model and competitive results with state of the art methods. The code and trained model are available on https://github.com/andrea-pilzer/unsup-stereo-depthGAN.

• The paper presents an unsupervised adversarial framework that uses cyclical generative networks to predict depth from stereo pairs without relying on ground truth labels.
• It employs dual sub-networks to generate forward and reverse disparity maps, enhancing accuracy through mutual reconstruction constraints.
• Experimental results on KITTI and Cityscapes show competitive performance and near real-time processing, highlighting its practical applicability.

Unsupervised Adversarial Depth Estimation using Cycled Generative Networks

The paper "Unsupervised Adversarial Depth Estimation using Cycled Generative Networks" introduces a novel approach to depth estimation in computer vision, circumventing the need for costly ground truth annotations. The authors employ an unsupervised approach leveraging adversarial learning mechanisms intertwined with cycled generative networks, marking a shift from traditional depth estimation techniques reliant on supervised learning.

Key Contributions

1. Adversarial Learning Framework: The work pioneers the application of adversarial learning frameworks for depth estimation. Unlike the supervised approaches requiring extensive labeled datasets, this unsupervised model leverages stereo image pairs to predict disparity maps, facilitating effective depth estimation.
2. Cycled Generative Network Structure: Their novel network architecture comprises dual generative sub-networks designed to predict forward and reverse disparity maps. These sub-networks operate in a cycle to reconstruct images from different views, effectively providing each other with constraints and oversight that refine their learning process.
3. Competitive Performance: Experimental results on benchmark datasets such as KITTI and Cityscapes exhibit the model's competitive performance against existing state-of-the-art techniques, particularly those also employing unsupervised methodologies.

Approach and Methodology

The core of the proposed method is a generative network organized in a cycle, departing from a traditional single-pathway generation. Each cycle consists of two sub-networks, which collaboratively learn and synthesize image views using adversarial learning. This innovative cycled approach helps to reinforce consistency in disparity maps across views. Moreover, the model's training benefits from adversarial loss, which encourages the production of more accurate image reconstructions, indirectly leading to more reliable depth maps.

Numerical Results and Validation

The paper details rigorous experimental validations on the KITTI and Cityscapes datasets. By employing metrics like mean relative error and RMSE, the performance is systematically evaluated, revealing substantial improvement over conventional stereo matching models and previous generative network approaches. Notably, the model maintains near real-time processing speeds, making it suitable for applications that demand fast computations.

Implications and Future Directions

The implications of this work are multifaceted. Practically, it illuminates a cost-effective path for deploying depth estimation in various domains without the encumbrance of labeled data. Theoretically, it opens avenues for further refinement of unsupervised learning frameworks, shaped by adversarial paradigms and cycle-consistent architectures.

Future research may delve into integrating attention mechanisms for enhanced feature representation and explore the potential of graphical models to ensure structured prediction in disparity outputs. Additionally, adapting this architecture to variations in stereo configurations and expanding to monocular settings may broaden its applicability.

In conclusion, this paper makes significant strides in the unsupervised depth estimation landscape, offering a robust framework that combines generative adversarial learning with cycle constraints for improved depth map accuracy. It stands as a promising template for upcoming explorations in both the methodologies for and applications of unsupervised depth estimation.