Learning Depth from Single Images with Deep Neural Network Embedding Focal Length (1803.10039v1)

Abstract: Learning depth from a single image, as an important issue in scene understanding, has attracted a lot of attention in the past decade. The accuracy of the depth estimation has been improved from conditional Markov random fields, non-parametric methods, to deep convolutional neural networks most recently. However, there exist inherent ambiguities in recovering 3D from a single 2D image. In this paper, we first prove the ambiguity between the focal length and monocular depth learning, and verify the result using experiments, showing that the focal length has a great influence on accurate depth recovery. In order to learn monocular depth by embedding the focal length, we propose a method to generate synthetic varying-focal-length dataset from fixed-focal-length datasets, and a simple and effective method is implemented to fill the holes in the newly generated images. For the sake of accurate depth recovery, we propose a novel deep neural network to infer depth through effectively fusing the middle-level information on the fixed-focal-length dataset, which outperforms the state-of-the-art methods built on pre-trained VGG. Furthermore, the newly generated varying-focal-length dataset is taken as input to the proposed network in both learning and inference phases. Extensive experiments on the fixed- and varying-focal-length datasets demonstrate that the learned monocular depth with embedded focal length is significantly improved compared to that without embedding the focal length information.

• The paper introduces a novel deep learning architecture that embeds focal length data to resolve depth ambiguities in single-image estimation.
• It leverages synthetic varying-focal-length datasets derived from fixed-focal-length sources to enhance practical applicability.
• Extensive evaluations on multiple benchmarks show that the method outperforms state-of-the-art models with lower error metrics and richer depth details.

Single-Image Depth Estimation with Deep Neural Networks Incorporating Focal Length

Depth estimation from single images presents a significant challenge within the field of computer vision, primarily due to inherent ambiguities in translating two-dimensional visual data into three-dimensional spatial information. The paper authored by Lei He, Guanghui Wang, and Zhanyi Hu addresses these challenges through the integration of focal length data into a deep learning framework specifically designed for depth inference from single images. This research demonstrates the advantages of utilizing focal length information to significantly enhance depth estimation accuracy.

Methodology

The core contribution of the paper lies in its innovative approach to incorporate focal length within a deep neural network architecture for depth estimation, a variable often overlooked in similar studies. The authors provide theoretical proofs illustrating the effects of focal length on monocular depth learning, highlighting the inherent ambiguities that arise during this process. By embedding focal length into their neural network architecture, these ambiguities can be mitigated.

The authors propose a method to create synthetic varying-focal-length datasets using fixed-focal-length datasets. This conversion is critical since existing datasets frequently utilize a uniform focal length, limiting their variability and practical applicability in scenarios where focal length differs. Their post-processing technique addresses any distortions resulting from this transformation, ensuring realistic depth predictions.

A novel neural network architecture is introduced that effectively fuses middle-level information from fixed-focal-length datasets, outperforming state-of-the-art methods that employ pre-trained VGG models. This network is characterized by the strategic integration of focal length data, optimizing both learning and inference stages. Through extensive experimental analysis across NYU, Make3D, KITTI, and SUNRGBD datasets, the research evidences superior depth inference capabilities when focal length data is utilized.

Experimental Evaluations

Quantitative analysis reveals that embedding focal length data into the depth estimation process results in a marked improvement in accuracy across various established datasets. Utilizing metrics such as average relative error, root mean squared error, and accuracy thresholds (e.g., $\delta < 1.25$), the paper demonstrates that the proposed network consistently achieves better performance than comparable models without the focal length consideration.

Furthermore, qualitative assessments provide visual confirmation of the richer structural details obtained through their approach. The network's architecture allows for detailed depth mappings which retain the fidelity of smaller-scale features in the scenes being analyzed.

Implications and Future Directions

The integration of focal length into single-image depth estimation paradigms presents both theoretical validation and practical applicability, expanding the potential use cases in scenarios such as autonomous driving and robotic vision, which often require dynamic adjustments to the focal length of imaging systems. This research also opens pathways for exploring the intersection of intrinsic camera parameters and machine learning in vision tasks.

Future developments may build upon this work by investigating additional intrinsic parameters beyond focal length, further refining depth estimation methods. Moreover, with rapid advancements in neural network architectures, there is potential for expanding this framework to accommodate more complex environmental setups and real-time applications.

The authors provide a foundational toolset and open their source code, inviting the research community to engage, adapt, and advance the concepts presented. By doing so, they contribute significantly to the ongoing dialogue centered around enhancing machine perception capabilities in variable focal length conditions.