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This technical report presents the 1st place winning solution for the WaymoOpen Dataset 3D semantic segmentation challenge 2022. Our network, termedLidarMultiNet, unifies the major LiDAR perception tasks such as 3D semanticsegmentation, object detection, and panoptic segmentation in a singleframework. At the core of LidarMultiNet is a strong 3D voxel-basedencoder-decoder network with a novel Global Context Pooling (GCP) moduleextracting global contextual features from a LiDAR frame to complement itslocal features. An optional second stage is proposed to refine the first-stagesegmentation or generate accurate panoptic segmentation results. Our solutionachieves a mIoU of 71.13 and is the best for most of the 22 classes on theWaymo 3D semantic segmentation test set, outperforming all the other 3Dsemantic segmentation methods on the official leaderboard. We demonstrate forthe first time that major LiDAR perception tasks can be unified in a singlestrong network that can be trained end-to-end.

LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network