计算机视觉
分割
人工智能
计算机科学
有效域
正多边形
反射(计算机编程)
凸优化
领域(数学分析)
凸组合
数学
几何学
数学分析
程序设计语言
作者
Yongjie Shi,Xianghua Ying,Hongbin Zha
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:23 (12): 24276-24289
标识
DOI:10.1109/tits.2022.3208334
摘要
Reflective convex mirrors are often used on street corners or as passenger-side mirrors on cars to obtain scene information by reflecting blind spots in the field of view, which can provide safety for pedestrians and drivers on roads, driveways, and alleys that lack of visibility. In recent years, deep learning based scene understanding methods (e.g., semantic segmentation) have been rapidly developed. However, due to gaps in the geometric domain, models trained on normal images are not directly applicable to scenes with convex mirror reflections. In this paper, we propose a novel framework to reduce the domain gap between normal images and convex mirror reflection images. In particular, we geometrically model convex mirrors to obtain a differentiable convex mirror simulation layer, CMSL. With the help of CMSL, we perform adversarial domain adaptation on edges in the input space and semantic boundaries in the output space to reduce the geometric appearance gap between the synthetic and real images. To verify the effectiveness of our algorithm, we construct the first convex mirror reflection scene dataset CMR1K, which contains 268 images with fine annotations. Extensive experimental results show that our algorithm can significantly outperform the baseline and previous methods. For example, our method surpasses the baseline and AdvEnt by 10% and 3% in mIoU, respectively.
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