Semantic scene completion (SSC) has recently gained popularity because it can provide both semantic and geometric information that can be used directly for autonomous vehicle navigation. However, there are still challenges to overcome. SSC is often hampered by occlusion and short-range perception due to sensor limitations, which can pose safety risks. This paper proposes a fundamental solution to this problem by leveraging vehicle-to-vehicle (V2V) communication. We propose the first generalized collaborative SSC framework that allows autonomous vehicles to share sensing information from different sensor views to jointly perform SSC tasks. To validate the proposed framework, we further build V2VSSC, the first V2V SSC benchmark, on top of the large-scale V2V perception dataset OPV2V. Extensive experiments demonstrate that by leveraging V2V communication, the SSC performance can be increased by 8.3% on geometric metric IoU and 6.0% mIOU.

翻译：语义场景补全（SSC）近年来受到广泛关注，因其能够同时提供可直接用于自动驾驶车辆导航的语义与几何信息。然而，该技术仍面临挑战：受传感器限制，SSC常因遮挡和短距离感知而受阻，可能引发安全隐患。本文提出一种利用车-车（V2V）通信解决该问题的根本性方案。我们首次提出通用协同SSC框架，允许自动驾驶车辆共享来自不同传感器视角的感知信息，以联合完成SSC任务。为验证该框架，本文进一步在大型V2V感知数据集OPV2V上构建了首个V2V SSC基准V2VSSC。大量实验表明，通过利用V2V通信，SSC在几何度量IoU和mIOU上可分别提升8.3%和6.0%。