Learning monocular depth estimation infusing traditional stereo knowledge (1904.04144v1)

Abstract: Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications. Recent works proved that this task could be learned without direct supervision from ground truth labels leveraging image synthesis on sequences or stereo pairs. Focusing on this second case, in this paper we leverage stereo matching in order to improve monocular depth estimation. To this aim we propose monoResMatch, a novel deep architecture designed to infer depth from a single input image by synthesizing features from a different point of view, horizontally aligned with the input image, performing stereo matching between the two cues. In contrast to previous works sharing this rationale, our network is the first trained end-to-end from scratch. Moreover, we show how obtaining proxy ground truth annotation through traditional stereo algorithms, such as Semi-Global Matching, enables more accurate monocular depth estimation still countering the need for expensive depth labels by keeping a self-supervised approach. Exhaustive experimental results prove how the synergy between i) the proposed monoResMatch architecture and ii) proxy-supervision attains state-of-the-art for self-supervised monocular depth estimation. The code is publicly available at https://github.com/fabiotosi92/monoResMatch-Tensorflow.

• The paper introduces monoResMatch, a deep learning architecture that infuses stereo matching principles to significantly enhance monocular depth estimation.
• It employs a multi-scale feature extractor, initial disparity estimation, and a refinement module, all trained end-to-end using a composite loss function.
• Empirical tests on the KITTI dataset demonstrate state-of-the-art accuracy, highlighting its potential for applications in autonomous systems and AR/VR technologies.

Analysis of "Learning Monocular Depth Estimation Infusing Traditional Stereo Knowledge"

The paper under discussion, "Learning Monocular Depth Estimation Infusing Traditional Stereo Knowledge," explores monocular depth estimation by leveraging concepts from stereo vision. Authored by Tosi et al., this research investigates how the principles of stereo matching can enhance the performance of monocular depth estimation, an area of computer vision with wide-ranging applications in autonomous systems and AR/VR technologies.

Key Contributions

The authors introduce a novel deep learning architecture called monoResMatch. This architecture is significant for its ability to infer depth from a single input image by synthesizing features from a virtual viewpoint and using a stereo matching process between real and synthesized images. Unlike previous attempts, this network is trained from scratch using an end-to-end approach. The architecture integrates stereo vision strategies, particularly Semi-Global Matching (SGM), to generate proxy ground truth labels for training, thereby addressing the lack of labeled depth data without costly annotations.

The architecture comprises three primary components:

1. Multi-scale Feature Extractor: This module derives high-level representations from the input image at various scales, making the architecture robust to photometric ambiguities.
2. Initial Disparity Estimation: Using an encoder-decoder setup, this stage outputs multi-scale disparity maps by emulating a virtual stereo setup, crucially bypassing the need for a stereo rig.
3. Disparity Refinement Module: This component adjusts the disparity outcomes by using feature space matching costs, integrating them through a correlation layer akin to those used in stereo systems.

The training involves optimizing a composite loss function that includes components for image reconstruction, disparity smoothness, and proxy supervision from SGM-generated labels.

Numerical Results

The empirical evaluation indicates that monoResMatch achieves state-of-the-art results in self-supervised monocular depth estimation. Testing on the KITTI dataset demonstrates superior accuracy over existing models, with enhancements visible across multiple metrics. The paper illustrates convincing improvement over models like monodepth and 3Net by integrating the proposed stereo-supervised approach.

Theoretical and Practical Implications

This work presents both theoretical insights and practical advancements in the field of depth estimation:

• Theoretical Insights: The paper provides a compelling case for the practical infusion of traditional stereo matching methodologies into monocular tasks, thereby enhancing depth estimation reliability without requiring additional hardware.
• Practical Applications: The implementation of monoResMatch potentially reduces the barriers associated with deploying depth-estimation systems in real-world environments that rely solely on monocular camera setups. The integration of stereo-based proxy supervision without necessitating dense ground-truth data simplifies deployment in cost-sensitive applications like robotics and AR systems.

Future Directions

The paper opens several pathways for future exploration:

1. Real-time Implementation: Enhancing the processing speeds of such architectures to enable real-time depth inference on mobile and embedded devices.
2. Generalization to New Domains: Adapting the model for varied environments beyond road-driving scenarios, such as indoor or cross-domain settings.
3. Extended Proxy-Supervision Techniques: Exploring additional techniques for synthetic label generation that could further reduce reliance on traditional depth sensing and expand this methodology to other tasks like semantic mapping.

In conclusion, Tosi et al.'s monoResMatch presents a significant advancement in leveraging stereo vision techniques for monocular depth estimation, providing a practical, self-supervised solution that bridges the performance gap between monocular and stereo systems. The paper's architecture and findings hold promising potential for expanding the application of depth perception technologies across diverse fields.