Source-linked AI summary
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
Dan Xu, Wei Wang, Hao Tang, Hong Liu, Nicu Sebe, Elisa Ricci
TL;DR
Monocular depth estimation seeks to recover pixel-level depth from a single RGB image. The paper integrates a structured attention model into a continuous CRF that fuses multi-scale CNN features, and reports competitive KITTI performance and state-of-the-art NYU Depth V2 results. The framework is trained end-to-end and outperforms most examined baselines, while stereo cues remain a future extension.
Problem
Monocular depth estimation requires recovering pixel-level depth from a single RGB image rather than multiple scene observations.
Method
A continuous CRF fuses multi-scale CNN features while structured attention regulates information transfer between related features and is jointly learned end-to-end.
Results
The method outperforms most state-of-the-art methods, is competitive on KITTI, and outperforms the state of the art on NYU Depth V2.
Takeaways & Limitations
Combining multi-scale information at feature level with structured attention improves depth estimates over previous CRF-CNN models.
Takeaways & Limitations
The framework does not use stereo cues, which are exploited by one compared method; integrating stereo information is identified as future work.
Abstract
from arXiv · showhide
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.
1. Introduction
Monocular depth estimation treats depth prediction from a single RGB image as supervised pixel-level regression. The paper proposes integrating structured attention into a CRF to regulate multi-scale CNN feature information and improve depth estimation.
- Monocular depth estimation predicts each pixel’s depth from an RGB image using supervised learning on image–depth pairs.
- CRFs have been used within deep architectures to fuse multi-scale information from CNN inner layers for pixel-level prediction.
- The proposed CRF operates directly on internal CNN feature representations rather than only prediction-level outputs.
- A structured attention mechanism automatically regulates information flow between related features at different scales.
- Attention variables are jointly estimated with multi-scale features during CRF inference, enabling end-to-end training of the architecture.
- Experiments report more accurate depth maps than traditional CRF and multi-scale CRF approaches, with comparisons on NYU Depth V2 and KITTI.
2. Related work
Prior monocular depth methods evolved from hand-crafted features to deep architectures, including CRF-based models for refining predictions and combining multi-scale CNN information. This work extends the closest multi-scale CRF approach by integrating attention mechanisms for monocular depth estimation.
- Monocular Depth Estimation: Earlier monocular depth estimation methods primarily used hand-crafted features, whereas recent approaches adopt deep architectures.
- Monocular Depth Estimation: Deep learning methods also explore joint prediction of depth with semantic segmentation, ego-motion, or surface normals, alongside unsupervised and weakly supervised reconstruction.
- Monocular Depth Estimation: Graphical models in deep architectures include hierarchical CRFs for refining CNN depth predictions and continuous CRFs for generating maps from CNN superpixel features.
- Monocular Depth Estimation: The closest prior work combines multi-scale information from multiple CNN layers; this paper further integrates attention to improve estimate accuracy.
- Fusing Multi-scale Information in CNNs: Multi-scale feature fusion has also been pursued through dilated convolutions, multi-resolution streams, skip connections, and deep supervision.
3. Estimating Depth Maps with Structured Attention Guided Conditional Neural Fields
The method combines a front-end CNN with a continuous CRF that jointly infers multi-scale latent features and structured attention maps for monocular depth estimation. Attention regulates information flow between intermediate and final CNN scales, enabling end-to-end training and iterative refinement of the final depth representation.
- 3.1. Problem Formulation and Overview: The architecture combines a front-end CNN with a CRF that fuses multi-scale feature maps for monocular depth estimation.Feature maps are resized to a common resolution before CRF processing.
- 3.2. Proposed Model: The CRF introduces attention variables that regulate information flow between each intermediate scale and the final scale.The learned attention maps identify which internal CNN information is relevant for final depth estimation.
- 3.2. Proposed Model: The CRF energy combines unary feature-observation terms, cross-scale feature relationships involving attention, and structural attention constraints.These terms respectively relate latent features to observations, model dependencies between final and intermediate scales, and regularize attention variables.
- 3.2. Proposed Model: Structural constraints encourage attention variables at related pixels and scales to be similar, while the model jointly infers attention maps and hidden features.The implementation limits attention dependencies to variables at the same scale to control computational cost.
- 3.2.3 Implementation with Neural Networks: Mean-field updates are implemented as a neural network, allowing simultaneous learning of CRF and front-end CNN parameters.The final depth map is obtained from the estimated representation at the last scale.
- 3.2.3 Implementation with Neural Networks: Attention and final-scale feature maps are iteratively updated using convolutional message passing, attention multiplication, unary terms, and sigmoid normalization.Intermediate-scale feature updates are omitted in the implementation to reduce computational overhead.
4. Experiments
Experiments evaluate the proposed approach on NYU Depth V2 and KITTI, comparing accuracy, computational efficiency, qualitative predictions, and component contributions. The method outperforms prior supervised methods on NYU Depth V2, offers a strong accuracy–time trade-off, and performs competitively in the KITTI monocular setting.
- Experimental design: Experiments use NYU Depth V2 and KITTI datasets, with comparisons against state-of-the-art methods and analyses of accuracy and computational efficiency.The evaluation includes qualitative depth-map examples and an ablation study of the method's components.
- Experimental setup: NYU Depth V2 experiments use 1,449 RGB-D pairs, with 795 for training and the remainder for testing, plus on-the-fly cropping, flipping, and scaling.Images are cropped to 320 × 240 pixels and scaled with ratios ρ ∈{1, 1.2, 1.5}.
- NYU Depth V2 results: The proposed approach outperforms all supervised learning methods using the original dataset and produces accurate predictions at object boundaries on NYU Depth V2.The paper highlights fine-grained recovery of details in objects such as chairs and tables.
- Efficiency analysis: The method achieves the best trade-off between accuracy and running time on NYU Depth V2, outperforming CRF-based baselines in accuracy and, for some comparisons, inference speed.Compared with, it is both more accurate under the original training set and faster because of different CRF potential functions.
- KITTI results: On KITTI, the approach outperforms previous supervised methods except [18], obtaining lower error but slightly inferior accuracy than that method.Methods [7], [9], and [18] use stereo information during training and are not directly comparable to the proposed monocular approach.
- Ablation study: The KITTI ablation study reports that CRFs improve performance and multi-scale feature fusion over naive concatenation, while structured attention further boosts performance.The analysis distinguishes ordinary attention from the structured attention model with a structured potential.
5. Conclusions
The paper presents a CRF that combines multi-scale CNN features using learned latent representations and structured attention, outperforming most baselines in monocular depth estimation.
- The proposed CRF combines multi-scale information from inner CNN layers through learned latent feature representations and an associated attention model.
- Structured attention at the feature level helps the approach outperform previous CRF-CNN depth estimation methods.
- The framework supports several CNN architectures and end-to-end training of the entire model.
- Extensive evaluation shows that the method outperforms most baselines.