Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation (1803.11029v1)

Abstract: Recent works have shown the benefit of integrating Conditional Random Fields (CRFs) models into deep architectures for improving pixel-level prediction tasks. Following this line of research, in this paper we introduce a novel approach for monocular depth estimation. Similarly to previous works, our method employs a continuous CRF to fuse multi-scale information derived from different layers of a front-end Convolutional Neural Network (CNN). Differently from past works, our approach benefits from a structured attention model which automatically regulates the amount of information transferred between corresponding features at different scales. Importantly, the proposed attention model is seamlessly integrated into the CRF, allowing end-to-end training of the entire architecture. Our extensive experimental evaluation demonstrates the effectiveness of the proposed method which is competitive with previous methods on the KITTI benchmark and outperforms the state of the art on the NYU Depth V2 dataset.

• The paper presents a novel end-to-end trainable model that integrates structured attention with CRF and CNN to enhance pixel-level depth estimation.
• It employs a structured attention mechanism to fuse multi-scale features, achieving superior performance on NYU Depth V2 and KITTI datasets with competitive error rates.
• The study demonstrates improved computational efficiency using a ResNet-50 backbone, paving the way for broader applications in semantic segmentation and autonomous driving.

Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation

The paper "Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation" presents a sophisticated approach to estimating depth from single RGB images, diverging from conventional multi-view based techniques. The methodology stands out by embedding a Conditional Random Fields (CRFs) model—an integral component for structured prediction—within a deep Convolutional Neural Network (CNN) framework. This integration effectively exploits multi-scale information that is typically latent within a CNN's layered architecture.

The core contribution of this research lies in the development and integration of a structured attention model, which mediates the flow of information across different scales. This model is designed such that it regulates the transfer of information between corresponding feature maps, subsequently refining depth estimation at the pixel level. By integrating this structured attention into the CRF, the authors ensure an end-to-end trainable system that has demonstrated superiority in both accuracy and efficiency compared to existing methods.

Key Highlights and Results

1. End-to-End Trainable Model: Unlike previous approaches that often require isolated optimization phases, this method is fully end-to-end trainable, streamlining the learning process across both the CNN and CRF components.
2. Structured Attention Mechanism: The introduction of a structured attention mechanism is notable. Previous works, while integrating CRFs, did not employ attention models to facilitate multi-scale fusion. This mechanism emphasizes salient features necessary for depth prediction, enhancing the model's accuracy.
3. Dataset Evaluation: The proposed model was evaluated on the NYU Depth V2 and KITTI datasets. On NYU Depth V2, it surpassed models trained on the same dataset, with a mean relative error (rel) of 0.125 and a root mean squared error (rms) of 0.593, highlighting its robustness in indoor environments. Its performance was also competitive with models trained on larger datasets. On the KITTI dataset, known for more challenging outdoor scenes, the approach achieved a rel of 0.122 and rms of 4.677, outperforming several state-of-the-art baselines.
4. Efficiency and Scalability: The architecture leverages a ResNet-50 backbone, complemented by the novel attention-guided CRF. The authors report substantial improvements in computation times while maintaining high accuracy, a critical factor for real-world applicability in scenarios like autonomous driving.

Implications and Future Directions

The implications of this work extend beyond monocular depth estimation. By demonstrating an efficient fusion of CRF with deep neural networks, it sets a precedent for other pixel-wise regression tasks, including semantic segmentation and object detection. The structured attention mechanism's ability to discern and focus on critical information across scales can potentially benefit multi-task learning frameworks and cross-domain applications.

Future investigations might explore integrating stereo vision cues within this architecture, potentially enhancing performance in scenes with ambiguous depth information. Furthermore, adapting this model to real-time applications could be a promising direction, especially for industries relying on quick and accurate environmental mapping.

In summary, this paper contributes a substantial advancement in leveraging structured attention within CRF-CNN hybrid models, marking a significant step forward in the field of single-image depth estimation while opening avenues for broadening its applications across various computer vision domains.