  A thorough and holistic scene understanding is crucial for autonomous
vehicles, where LiDAR semantic segmentation plays an indispensable role.
However, most existing methods focus on the network design while neglecting the
inherent difficulty, imbalanced data distribution in the realistic dataset
(also named long-tailed distribution), which narrows down the capability of
state-of-the-art methods. In this paper, we propose an input-output balanced
framework to handle the issue of long-tailed distribution. Specifically, for
the input space, we synthesize these tailed instances from mesh models and well
simulate the position and density distribution of LiDAR scan, which enhances
the input data balance and improves the data diversity. For the output space, a
multi-head block is proposed to group different categories based on their
shapes and instance amounts, which alleviates the biased representation of
dominating category during the feature learning. We evaluate the proposed model
on two large-scale datasets, SemanticKITTI and nuScenes, where state-of-the-art
results demonstrate its effectiveness. The proposed new modules can also be
used as a plug-and-play, and we apply them on various backbones and datasets,
showing its good generalization ability.
