This technical report presents the 1st place winning solution for the Waymo
Open Dataset 3D semantic segmentation challenge 2022. Our network, termed
LidarMultiNet, unifies the major LiDAR perception tasks such as 3D semantic
segmentation, object detection, and panoptic segmentation in a single
framework. At the core of LidarMultiNet is a strong 3D voxel-based
encoder-decoder network with a novel Global Context Pooling (GCP) module
extracting global contextual features from a LiDAR frame to complement its
local features. An optional second stage is proposed to refine the first-stage
segmentation or generate accurate panoptic segmentation results. Our solution
achieves a mIoU of 71.13 and is the best for most of the 22 classes on the
Waymo 3D semantic segmentation test set, outperforming all the other 3D
semantic segmentation methods on the official leaderboard. We demonstrate for
the first time that major LiDAR perception tasks can be unified in a single
strong network that can be trained end-to-end.

LidarMultiNet 利用深度神经网络，将 3D 语义分割、物体检测和全景分割等核心 LiDAR 感知任务统一在单一框架中，实现了最佳表现。