Structure-Aware Residual Pyramid Network for Monocular Depth Estimation (1907.06023v1)

Abstract: Monocular depth estimation is an essential task for scene understanding. The underlying structure of objects and stuff in a complex scene is critical to recovering accurate and visually-pleasing depth maps. Global structure conveys scene layouts, while local structure reflects shape details. Recently developed approaches based on convolutional neural networks (CNNs) significantly improve the performance of depth estimation. However, few of them take into account multi-scale structures in complex scenes. In this paper, we propose a Structure-Aware Residual Pyramid Network (SARPN) to exploit multi-scale structures for accurate depth prediction. We propose a Residual Pyramid Decoder (RPD) which expresses global scene structure in upper levels to represent layouts, and local structure in lower levels to present shape details. At each level, we propose Residual Refinement Modules (RRM) that predict residual maps to progressively add finer structures on the coarser structure predicted at the upper level. In order to fully exploit multi-scale image features, an Adaptive Dense Feature Fusion (ADFF) module, which adaptively fuses effective features from all scales for inferring structures of each scale, is introduced. Experiment results on the challenging NYU-Depth v2 dataset demonstrate that our proposed approach achieves state-of-the-art performance in both qualitative and quantitative evaluation. The code is available at https://github.com/Xt-Chen/SARPN.

• The paper presents a novel Residual Pyramid Decoder that refines depth maps using hierarchical residual refinement modules.
• It introduces an Adaptive Dense Feature Fusion module that selectively merges multi-scale features for improved depth reconstruction.
• Experimental results on NYU-Depth v2 demonstrate state-of-the-art performance with enhanced visual fidelity and structural accuracy.

Structure-Aware Residual Pyramid Network for Monocular Depth Estimation

The paper presents a novel approach for monocular depth estimation through the introduction of a Structure-Aware Residual Pyramid Network (SARPN). Monocular depth estimation remains a critical challenge in scene understanding, requiring accurate reconstruction of depth maps from single RGB images. Notably, understanding the multi-scale structure of complex scenes significantly enhances the precision of such reconstructions.

The authors address the deficiencies in previous CNN-based methods, which often overlook the multi-scale nature of scene structures, especially in maintaining the details of diverse object sizes and shapes within a scene. They propose a Residual Pyramid Decoder (RPD) that leverages hierarchical structural predictions, offering a more articulated recovery of scene depth by using what they denote as Residual Refinement Modules (RRM). These modules focus on adding fine-grained detail to larger structures iteratively, resulting in more accurate depth predictions.

Critical to this approach is the Adaptive Dense Feature Fusion (ADFF) module, which facilitates the selective fusion of useful features across multiple scales. This integration allows the network to adaptively respond to varying feature requirements essential for different depth levels, optimizing the depth estimation process effectively.

Compared with existing methodologies, the experimental results on the NYU-Depth v2 dataset illustrate significant improvements. The method achieves state-of-the-art performance, particularly in maintaining visual fidelity and structural integrity in the produced depth maps. The quantitative evaluations demonstrate improvements over other leading solutions, reflected in various key metrics like REL, RMS, and accuracy thresholds.

From a theoretical perspective, this research emphasizes the importance of structure-awareness in depth estimation networks. By employing a hierarchical approach, it successfully integrates multi-scale feature representation, which could inspire future architectures for diverse applications in computer vision, including object recognition and segmentation. Practically, the refined depth predictions can leverage advancements in augmented reality, robotic navigation, and visual effects industries.

Future work might explore this architecture's adaptability to other forms of data, such as those captured in outdoor environments or with varying lighting conditions. Likewise, the model's efficiency and scalability can be further optimized by investigating alternative architectures or training regimes. Given the depth and versatility of neural networks, exploring more advanced forms of feature fusion and integration techniques might yield even greater insights into depth estimation capabilities.