基于生成对抗网络的前视图像素级鸟瞰图生成

Tianru Zhou, Dong He, Chang-Hee Lee
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引用次数: 2

摘要

准确地理解交通场景是自动驾驶的基础,而鸟瞰图是创建周围全景的重要组成部分。然而,由于两个域在颜色、汽车类型、地标和遮挡方面存在很大差距,因此从前视图合成相关鸟瞰图的任务非常具有挑战性。因此,我们提出了一个不同的鸟瞰图生成框架,其中我们的方法使用了一个包含一个生成器和两个鉴别器的网络。该生成器由一个编码器和一个解码器组成,真假鉴别器由原始GAN启发,识别鉴别器被设计用于提高源域和目标域之间的相关性。与以前的方法相比,我们的方法既不使用基于几何的转换,也不使用中间视图方法。我们提出的网络成功地从前视图合成了相关的鸟瞰图,具有更清晰的细节和更高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pixel-Level Bird View Image Generation from Front View by Using a Generative Adversarial Network
Understanding traffic scenes robustly is a cornerstone for autonomous driving, where bird view is an essential component to create panoramas of surroundings. However, since a large gap of two domains in terms of color, types of cars, landmarks, and occlusions, the task of synthesizing the associated bird view from a front view is quite challenging. Therefore, we propose a different framework for a bird view generation, where our approach employs a network that contains one generator and two discriminators. The generator consists of an encoder and a decoder, the real/fake discriminator inspired by the original GAN, and the identification discriminator has been designed to improve relevance between the source and the target domains. When compared with other previous methods, our approach utilizes neither geometry-based transformation nor an intermediate view approach. Our proposed network successfully synthesizes the associated bird view from a front view with sharper details and higher accuracy.
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