Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation (2108.08829v1)

Abstract: Self-supervised monocular depth estimation has been widely studied, owing to its practical importance and recent promising improvements. However, most works suffer from limited supervision of photometric consistency, especially in weak texture regions and at object boundaries. To overcome this weakness, we propose novel ideas to improve self-supervised monocular depth estimation by leveraging cross-domain information, especially scene semantics. We focus on incorporating implicit semantic knowledge into geometric representation enhancement and suggest two ideas: a metric learning approach that exploits the semantics-guided local geometry to optimize intermediate depth representations and a novel feature fusion module that judiciously utilizes cross-modality between two heterogeneous feature representations. We comprehensively evaluate our methods on the KITTI dataset and demonstrate that our method outperforms state-of-the-art methods. The source code is available at https://github.com/hyBlue/FSRE-Depth.

• The paper introduces a novel semantic integration approach for self-supervised monocular depth estimation using a semantics-guided metric learning strategy.
• It employs a cross-task multi-embedding attention module to fuse features from depth estimation and semantic segmentation, boosting prediction accuracy.
• Extensive evaluations on the KITTI dataset demonstrate reduced error metrics and improved accuracy thresholds, underscoring its practical impact in autonomous driving and robotics.

Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation

The paper introduces a novel approach to improving self-supervised monocular depth estimation by incorporating semantic information to enhance geometric representations. The primary innovation lies in the integration of semantics-aware learning within the domain of self-supervised depth estimation, which traditionally suffers from challenges like limited photometric consistency, especially in regions with weak textures or complex object boundaries.

Key Contributions

1. Semantic Integration: The authors propose leveraging cross-domain information—specifically scene semantics—to address existing limitations in monocular depth estimation. Two critical elements of this integration are emphasized: a robust metric learning approach and an effective feature fusion module.
2. Metric Learning with Semantic Guidance: A semantics-guided triplet loss is developed to optimize intermediate depth representations. This loss function utilizes local geometric cues derived from semantic understanding to refine feature distinctions, especially near object boundaries, enhancing the overall depth prediction accuracy.
3. Cross-task Feature Fusion: The authors introduce a cross-task multi-embedding attention (CMA) module that facilitates the fusion of features from depth estimation and semantic segmentation tasks. The module capitalizes on cross-modal interactions to yield more consistent depth features across semantic contexts.
4. Comprehensive Evaluation: Extensive experimentation on the KITTI dataset illustrates that the proposed methodologies surpass state-of-the-art approaches in performance metrics, substantiating the efficacy of semantic integration in depth estimation tasks.

Results and Implications

The architecture significantly improves depth estimation accuracy, with quantitative assessments showing enhancement across all standard depth prediction metrics. Depth errors such as AbsRel, SqRel, RMS, and RMSlog showed reduced values, while accuracy thresholds ($<1.25$, $<1.25^2$, $<1.25^3$) improved markedly compared to previous models.

The introduced methods demonstrate substantial potential in applications such as autonomous driving and robotics, where precise depth estimation is vital. The inclusion of semantic knowledge not only mitigates issues caused by weak textures and boundary ambiguities but also aligns well with the growing trend of multitask learning frameworks that aim to extract and exploit joint information across multiple related tasks.

Future Directions

This research opens avenues for further refinement in semantics-driven depth prediction methodologies. Future work could explore:

• Generalization to Diverse Environments: Extending the applicability of the model to work robustly across diverse environments and less structured scenes.
• Integration with Other Modalities: Investigating the fusion of additional modalities, such as motion cues or temporal information, for even richer scene understanding.
• Optimization for Real-time Applications: Streamlining the model to operate efficiently in real-time, making it more apt for dynamic environments encountered in real-world applications.

In conclusion, this paper highlights a significant step forward by innovatively integrating semantic information into the field of self-supervised depth estimation, providing tangible improvements and highlighting the importance of cross-domain learning in modern computer vision tasks.