Deeper Depth Prediction with Fully Convolutional Residual Networks (1606.00373v2)

Abstract: This paper addresses the problem of estimating the depth map of a scene given a single RGB image. We propose a fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps. In order to improve the output resolution, we present a novel way to efficiently learn feature map up-sampling within the network. For optimization, we introduce the reverse Huber loss that is particularly suited for the task at hand and driven by the value distributions commonly present in depth maps. Our model is composed of a single architecture that is trained end-to-end and does not rely on post-processing techniques, such as CRFs or other additional refinement steps. As a result, it runs in real-time on images or videos. In the evaluation, we show that the proposed model contains fewer parameters and requires fewer training data than the current state of the art, while outperforming all approaches on depth estimation. Code and models are publicly available.

• The paper introduces a novel fully convolutional residual network that replaces fully connected layers with efficient up-projection blocks for high-resolution depth estimation.
• It leverages the ResNet-50 backbone and a dynamic reverse Huber loss to significantly outperform existing methods on NYU Depth v2 and Make3D datasets.
• The approach enhances feature propagation and efficiency, offering practical benefits for applications in autonomous driving, robotics, and augmented reality.

Deeper Depth Prediction with Fully Convolutional Residual Networks

The paper, "Deeper Depth Prediction with Fully Convolutional Residual Networks," introduces a novel methodology for depth estimation from a single RGB image. This research tackles an inherently ill-posed problem due to the absence of direct depth cues available from monocular images. The proposed solution employs a convolutional neural network (CNN) enhanced with residual learning techniques and novel up-sampling blocks, providing a significant advancement over prior approaches.

Architecture and Methodology

The core of the paper's approach lies in its fully convolutional network design, structured around the ResNet-50 architecture due to its large receptive field which is crucial for capturing global context in high-dimensional regression tasks like depth prediction. The traditional fully connected layers are replaced with a series of up-sampling blocks, referred to as "up-projection" blocks, enhancing computational efficiency and scalability. These blocks build upon the standard up-convolutional layers by integrating residual connections, facilitating higher resolution outputs with fewer parameters and avoiding the explosive increase in parameter count seen in models using dense fully-connected layers.

The up-projection blocks utilize unpooling followed by convolution, and a second projection path to include high-level information from earlier layers. This innovative design promotes more effective feature propagation and enhances depth map resolution while maintaining manageable computational complexity. The architecture progresses through a sequence of up-projections, each capable of doubling the resolution, forming a progressively richer and more detailed depth map output.

Optimization and Loss Function

A notable aspect of this work is the optimization via the reverse Huber (berHu) loss function, which balances the $\mathcal{L}_1$ and $\mathcal{L}_2$ norms, adapting dynamically based on the residuals. This hybrid approach enhances robustness to outliers and improves the convergence properties of the training process. The berHu loss focuses on minimizing small residual errors more effectively than the $\mathcal{L}_2$ loss alone, particularly benefiting metrics sensitive to relative errors.

Experimental Results

The validation of the proposed model is carried out on the NYU Depth v2 and Make3D datasets. On NYU Depth v2, the model outperforms existing methods by a notable margin across several metrics. For instance, it achieves relative error (rel) and root mean square error (rms) of 0.127 and 0.573 respectively, demonstrating superior performance in both accuracy and detail preservation. Compared to the multi-scale approaches by Eigen and Fergus, the single-scale ResNet-UpProj model shows significant qualitative improvements in edge retention and structural coherence.

On the Make3D dataset, the fully convolutional architecture also surpasses previous state-of-the-art results, achieving a relative error of 0.176 and an rms of 4.46. These results validate the model's robustness across different environments, from indoor to outdoor scenes, demonstrating the adaptability of the proposed depth prediction methodology.

Implications and Future Directions

The practical implications of this research span various applications where depth information is crucial yet direct sensing is infeasible due to economic or technical constraints. Enhanced single-image depth prediction can benefit autonomous driving, robotics, augmented reality, and 3D scene reconstruction, particularly in resource-limited settings.

Theoretically, this work advances our understanding of the interplay between network depth, receptive fields, and up-sampling mechanisms in high-dimensional regression problems. The introduction of up-projection blocks opens new avenues for designing efficient neural architectures in other domains requiring fine-grained output resolution, such as semantic segmentation or super-resolution tasks.

Future developments may focus on integrating these architectural advancements with newer variants of residual networks, exploring alternative loss functions further fine-tuned to specific applications, or combining this approach with sequence modeling techniques to exploit temporal information in video streams for even more accurate and coherent depth predictions.

Conclusion

"Deeper Depth Prediction with Fully Convolutional Residual Networks" represents a significant step forward in monocular depth estimation, offering a scalable, efficient, and highly accurate solution. The proposed model, validated through extensive experiments, successfully balances the complexity and performance demands of high-dimensional regression tasks, setting a new benchmark for future research in the field.