Enhanced Boundary Learning for Glass-like Object Segmentation (2103.15734v2)

Abstract: Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module that outputs finer boundary cues. We then introduce an edge-aware point-based graph convolution network module to model the global shape along the boundary. We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours. Both modules are lightweight and effective: they can be embedded into various segmentation models. In extensive experiments on three recent glass-like object segmentation datasets, including Trans10k, MSD, and GDD, our approach establishes new state-of-the-art results. We also illustrate the strong generalization properties of our method on three generic segmentation datasets, including Cityscapes, BDD, and COCO Stuff. Code and models is available at \url{https://github.com/hehao13/EBLNet}.

• The paper introduces a novel boundary refinement technique using RDM and PGM to significantly enhance segmentation accuracy for glass-like objects.
• It refines both coarse and fine boundary details by combining differential, morphological, and graph convolutional strategies.
• Experimental results show a 3-5% mIoU improvement, demonstrating improved edge detection and robust performance across diverse datasets.

Enhanced Boundary Learning for Glass-like Object Segmentation

Glass-like object segmentation presents unique challenges in computer vision due to the inherent properties of these objects, such as transparency and varied appearances, which confound traditional segmentation methods. The paper proposes a novel approach to improve segmentation accuracy for glass-like objects, leveraging enhanced boundary learning strategies.

Summary of the Approach

The research introduces two primary modules: the Refined Differential Module (RDM) and the edge-aware Point-based Graph Convolution Network (PGM). These modules offer complementary techniques that can be integrated into pre-existing segmentation models to enhance their performance on glass-like object scenarios.

1. Refined Differential Module (RDM): This module operates on both coarse and fine levels. It uniquely supervises edge and non-edge regions, drawing from differential and morphological processing techniques. The RDM refines boundary delineation by mitigating noise from the glass objects' inner parts, thereby producing more accurate segmentation contours.
2. Point-based Graph Convolution Network (PGM): Post edge refinement, PGM utilizes spatial correlations among boundary points to globally enhance feature representation, further improving the predictive accuracy of object borders.

These modules are designed to be lightweight and adaptable across various segmentation architectures, ensuring broad applicability and ease of integration.

Experimental Results

Through comprehensive experiments conducted on datasets specifically curated for glass-like object segmentation—Trans10k, GDD, and MSD—the proposed method consistently outperformed existing strategies. Quantitative measures indicated an improvement of approximately 3-5% in mean Intersection over Union (mIoU) compared to prior best-performing methods. This performance gain, accompanied by enhanced boundary prediction accuracy, underscores the efficacy of the approach.

Implications and Future Prospects

The implications of this research span both practical applications, such as robotics navigation and object manipulation in dynamic environments, and theoretical advancements in segmentation algorithms. The method's generalization capabilities were demonstrated through further testing on standard segmentation datasets like Cityscapes, BDD, and COCO Stuff, indicating robustness beyond the scope of glass-like objects.

Future advancements could explore deepening the integration of boundary information across broader contexts or developing adaptive learning strategies that refine boundary detection dynamically based on scene complexity. Continued research could also focus on optimizing computational resource use for real-time applications, pushing the boundaries of efficiency and scalability in large-scale deployments.

Conclusion

This paper contributes to the field of computer vision by addressing the niche but challenging problem of glass-like object segmentation. By innovatively focusing on boundary refinement, it sets a precedent for incorporating nuanced edge information into segmentation tasks. The scalability and applicability of these modules could pave the way for further exploration in environments where boundary clarity is pivotal, extending the potential of intelligent vision systems.