Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation

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.

Accepted at CVPR 2018 as a spotlight

References in corpus (12)

• Deep Residual Learning for Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
• Fully Convolutional Networks for Semantic Segmentation
• DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling
• Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
• Unsupervised Learning of Depth and Ego-Motion from Video
• Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
• Semi-Supervised Deep Learning for Monocular Depth Map Prediction
• Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
• Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference

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• Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes
• S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
• A Parametric Top-View Representation of Complex Road Scenes
• Understanding Road Layout from Videos as a Whole