  Range-view based LiDAR segmentation methods are attractive for practical
applications due to their direct inheritance from efficient 2D CNN
architectures. In literature, most range-view based methods follow the
per-pixel classification paradigm. Recently, in the image segmentation domain,
another paradigm formulates segmentation as a mask-classification problem and
has achieved remarkable performance. This raises an interesting question: can
the mask-classification paradigm benefit the range-view based LiDAR
segmentation and achieve better performance than the counterpart per-pixel
paradigm? To answer this question, we propose a unified mask-classification
model, MaskRange, for the range-view based LiDAR semantic and panoptic
segmentation. Along with the new paradigm, we also propose a novel data
augmentation method to deal with overfitting, context-reliance, and
class-imbalance problems. Extensive experiments are conducted on the
SemanticKITTI benchmark. Among all published range-view based methods, our
MaskRange achieves state-of-the-art performance with $66.10$ mIoU on semantic
segmentation and promising results with $53.10$ PQ on panoptic segmentation
with high efficiency. Our code will be released.
