Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries (1803.08673v2)

Abstract: This paper considers the problem of single image depth estimation. The employment of convolutional neural networks (CNNs) has recently brought about significant advancements in the research of this problem. However, most existing methods suffer from loss of spatial resolution in the estimated depth maps; a typical symptom is distorted and blurry reconstruction of object boundaries. In this paper, toward more accurate estimation with a focus on depth maps with higher spatial resolution, we propose two improvements to existing approaches. One is about the strategy of fusing features extracted at different scales, for which we propose an improved network architecture consisting of four modules: an encoder, decoder, multi-scale feature fusion module, and refinement module. The other is about loss functions for measuring inference errors used in training. We show that three loss terms, which measure errors in depth, gradients and surface normals, respectively, contribute to improvement of accuracy in an complementary fashion. Experimental results show that these two improvements enable to attain higher accuracy than the current state-of-the-arts, which is given by finer resolution reconstruction, for example, with small objects and object boundaries.

• The paper demonstrates improved network architecture that effectively fuses multi-scale features for higher resolution depth maps.
• The paper introduces a novel loss function combining depth, gradients, and normals to capture accurate object boundaries.
• Results on the NYU-Depth V2 dataset show significant gains in accuracy metrics, evidencing superior edge and detail recovery.

Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries

The paper "Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries" addresses key challenges in the field of single image depth estimation through convolutional neural networks (CNNs). Authored by Hu, Ozay, Zhang, and Okatani, the research targets the common shortcoming of spatial resolution loss, evident in current depth map estimations, particularly around object boundaries.

Key Contributions

This paper proposes two main advancements over existing methods: architectural improvements for feature fusion and an enhanced formulation of the loss function for training. These innovations aim to refine depth maps to higher spatial resolutions with precision in object boundaries.

1. Improved Network Architecture: Their approach revolves around an enhanced CNN framework that integrates an encoder, decoder, multi-scale feature fusion module, and a refinement module. This architecture is designed to amalgamate spatial features from different network layers effectively. The multi-scale feature fusion module plays a crucial role in aligning features from various CNN layers through upscaling and convolution, ensuring that depth cues from different scales are utilized in a complementary manner.
2. Enhanced Loss Function: The paper critiques the prevalent use of single-criterion loss functions, commonly based on depth difference, for their lack of sensitivity to edge and boundary details. In response, it introduces a multi-faceted loss function that accounts for depth, gradients, and surface normals. By incorporating complementary loss terms, the model is able to refine details at object boundaries and yield smoother, more detailed depth maps.

Empirical Findings

Experimentation on the NYU-Depth V2 dataset elucidates that this novel approach surpasses previous state-of-the-art methods in terms of several depth estimation metrics. The experiments validate the effectiveness of both architectural and loss function improvements, with remarkable performance in recovering fine details and maintaining clear object boundaries.

• Resolution and Accuracy: The proposed model achieves notable higher accuracy metrics as enumerated in RMS, REL, and $\log_{10}$ error measurements, alongside improved threshold accuracies ($\delta < 1.25$, $\delta < 1.25^2$, and $\delta < 1.25^3$).
• Edge and Detail Recovery: Compared to prior work such as Eigen and Fergus or Laina et al., the proposed method demonstrates superior edge recovery performance, supporting application in tasks requiring precision, such as object recognition or depth-aware rendering.

Implications and Future Directions

The implications of this work extend to real-world applications where precise depth perception from a single image is beneficial. This includes areas in robotics, augmented reality, and autonomous driving where spatial resolution and depth accuracy are critical.

Future developments may explore integrating this architecture with more complex tasks or employing unsupervised learning paradigms to further reduce computational overhead while maintaining or improving depth estimation accuracy. Extending this framework to accommodate varying environmental conditions and diverse datasets could enhance its generalization capability.

In summary, this paper provides valuable insights and technical advancements for single image depth estimation, addressing significant gaps in spatial accuracy and resolution. It opens avenues for practical implementation and future research in AI-driven depth perception methodologies.