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.

通过车到车通信，提出了一种解决语义场景补全 (SSC) 中的遮挡和短距离感知等问题的新框架，实验证明通过车到车通信可以提高 8.3％的几何度量 IoU 和 6.0％的平均 IoU (SSC 性能)。