Depth Map Prediction from a Single Image using a Multi-Scale Deep Network (1406.2283v1)

Abstract: Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring integration of both global and local information from various cues. Moreover, the task is inherently ambiguous, with a large source of uncertainty coming from the overall scale. In this paper, we present a new method that addresses this task by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally. We also apply a scale-invariant error to help measure depth relations rather than scale. By leveraging the raw datasets as large sources of training data, our method achieves state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation.

• The paper presents a dual-network approach integrating global and local cues to accurately predict depth maps from a single image.
• It employs a coarse-scale network for holistic scene understanding and a fine-scale network for refining local details, significantly reducing depth errors.
• The method outperforms previous approaches on NYU Depth v2 and KITTI datasets, demonstrating its potential for robotics and autonomous systems.

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

The paper presents an innovative method for predicting depth maps from a single image by leveraging a multi-scale deep network architecture. The proposed approach addresses the inherent difficulties in monocular depth estimation by integrating both global and local information through a dual network structure.

Methodology

The core methodology involves two distinct deep neural network stacks:

1. Global Coarse-Scale Network: This network predicts a holistic, coarse depth map of the entire scene. It utilizes a global perspective to capture essential features such as vanishing points, object locations, and room alignments. The five-layer convolutional network, followed by two fully connected layers, enables this global understanding of the scene. Pretrained on ImageNet for enhanced feature extraction, this network focuses on integrating information across the image to form a comprehensive depth structure.
2. Local Fine-Scale Network: Complementing the coarse predictions, the fine-scale network performs local refinements to improve depth prediction accuracy around object borders and fine details. This stack uses the output of the global network as an additional input feature, processing it alongside the original image to refine depth estimations locally.

A notable contribution is the use of a scale-invariant error metric, aiming to prioritize spatial relationships over absolute scale. The training objective function integrates this scale-invariant aspect with traditional pointwise errors, thereby balancing absolute depth accuracy with the preservation of depth relations.

Results

The proposed method achieves state-of-the-art results on two benchmark datasets: NYU Depth v2 and KITTI. Key performance metrics illustrate significant improvements:

• NYU Depth v2: The method surpasses existing approaches such as Make3D and those by Karsch et al. and Ladicky et al., with substantial gains across all evaluated error metrics. Specifically, the method achieves an RMSE (log) of 0.283, compared to 0.409 by Make3D.
• KITTI: Similarly, on this outdoor dataset, the method demonstrates an average 31% improvement over Make3D, emphasizing its robustness across diverse environments.

The qualitative results, depicted in the paper, showcase the efficiency of the fine-scale network in enhancing local depth details, aligning predictions more accurately with object and wall edges.

Implications and Future Directions

The implications of this research are manifold:

1. Practical Applications: Accurate depth prediction facilitates advancements in 3D modeling, robotic navigation, and scene understanding, essential for various applications ranging from autonomous driving to augmented reality.
2. Theoretical Insights: The dual network architecture offers a novel perspective on balancing global and local depth cues, potentially influencing future methodologies in depth estimation and other related fields.

Future work may explore integrating additional geometric information, such as surface normals, to further refine depth predictions. Extending the technique to higher resolutions, potentially through hierarchical multi-scale network applications, could also enhance practical usability.

In conclusion, this paper's methodological advancements and robust numerical results substantiate its contributions to single-image depth prediction, presenting a comprehensive framework capable of addressing the complex demands of this challenging task.