V2VSSC: A 3D Semantic Scene Completion Benchmark for Perception with
  Vehicle to Vehicle Communication
Yuanfang Zhang Junxuan Li Kaiqing Luo Yiying Yang Jiayi Han Nian Liu Denghui Qin Peng Han Chengpei Xu
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.