Occlusion-Aware Seamless Segmentation (2407.02182v3)

Abstract: Panoramic images can broaden the Field of View (FoV), occlusion-aware prediction can deepen the understanding of the scene, and domain adaptation can transfer across viewing domains. In this work, we introduce a novel task, Occlusion-Aware Seamless Segmentation (OASS), which simultaneously tackles all these three challenges. For benchmarking OASS, we establish a new human-annotated dataset for Blending Panoramic Amodal Seamless Segmentation, i.e., BlendPASS. Besides, we propose the first solution UnmaskFormer, aiming at unmasking the narrow FoV, occlusions, and domain gaps all at once. Specifically, UnmaskFormer includes the crucial designs of Unmasking Attention (UA) and Amodal-oriented Mix (AoMix). Our method achieves state-of-the-art performance on the BlendPASS dataset, reaching a remarkable mAPQ of 26.58% and mIoU of 43.66%. On public panoramic semantic segmentation datasets, i.e., SynPASS and DensePASS, our method outperforms previous methods and obtains 45.34% and 48.08% in mIoU, respectively. The fresh BlendPASS dataset and our source code are available at https://github.com/yihong-97/OASS.

• The paper introduces OASS, a unified approach that integrates field-of-view expansion, occlusion prediction, and domain adaptation for seamless segmentation.
• It presents the BlendPASS dataset as a robust benchmarking tool for evaluating segmentation performance on panoramic images.
• The proposed UnmaskFormer model achieves state-of-the-art results with 26.58% mAPQ and 43.66% mIoU, advancing comprehensive scene understanding.

Overview of Occlusion-Aware Seamless Segmentation

The paper "Occlusion-Aware Seamless Segmentation" by Yihong Cao et al. proposes a comprehensive framework tackling panoramic scene understanding through a novel task known as Occlusion-Aware Seamless Segmentation (OASS). This task is designed to address three significant challenges connected to the panoramic domain: broadening the field of view (FoV), predicting occlusion, and adapting across different domains. The paper introduces BlendPASS, a human-annotated dataset specifically for benchmarking OASS, and further, presents the UnmaskFormer, a pioneering method to mitigate these challenges. This paper lays foundational work significant for researchers interested in combining panoramic image processing with semantic and amodal segmentation.

Main Contributions

1. Introduction of OASS: OASS integrates three critical tasks: expanding FoV, handling occlusions, and bridging domain adaptation, all within seamless segmentation. This innovative approach enhances comprehensive scene understanding by combining tasks usually addressed individually.
2. BlendPASS Dataset: The creation of the BlendPASS dataset fills a gap in resources for evaluating OASS. This dataset is extensive, providing a robust platform for testing segmentation tasks involving panoramic images.
3. UnmaskFormer Model: The UnmaskFormer is introduced as the first integrated solution to confront the limitations posed by narrow FoV, occlusions, and domain gaps collectively. This model includes advanced components like Unmasking Attention (UA) and Amodal-oriented Mix (AoMix) to improve predictive accuracy in challenging panoramic image environments.
4. Benchmark Results: The provided results demonstrate that UnmaskFormer achieves superior performance on OASS tasks across several datasets, notably obtaining an mAPQ of 26.58% and mIoU of 43.66% on the BlendPASS dataset. It demonstrates enhanced results on established public datasets: SynPASS and DensePASS, with notable improvements over existing methodologies.

Numerical Results

The authors quantify the effectiveness of UnmaskFormer under the OASS framework by benchmarking against existing methods on various challenging datasets. UnmaskFormer attains state-of-the-art results, surpassing leading models by notable margins. For instance, on the BlendPASS dataset, UnmaskFormer achieves 26.58% mAPQ, marking a significant advancement over comparable methods.

Implications and Future Directions

The proposed approach significantly impacts both practical and theoretical domains, particularly in autonomous driving and advanced scene understanding applications. The ability to predict occluded objects while adapting across domains presents an appealing advantage. Moreover, the research implies potential progressions in AI where comprehensive environmental understanding from various data forms is crucial, like robotics and urban planning.

Potential future research could explore broader integration with real-time processing applications and refine approaches further to reduce computational costs. Another interesting direction would be exploring the integration of UnmaskFormer with other sensor modalities, such as LiDAR, to enrich the occlusion-aware understanding.

In conclusion, this research represents substantial progress towards panoramic and semantic scene understanding, offering a robust base for further exploration and innovation. By addressing the challenges of panoramic domain distortion, occlusion handling, and seamless segmentation together, it sets a new path forward for advancing real-world applicability of advanced segmentation models.