  Projecting the point cloud on the 2D spherical range image transforms the
LiDAR semantic segmentation to a 2D segmentation task on the range image.
However, the LiDAR range image is still naturally different from the regular 2D
RGB image; for example, each position on the range image encodes the unique
geometry information. In this paper, we propose a new projection-based LiDAR
semantic segmentation pipeline that consists of a novel network structure and
an efficient post-processing step. In our network structure, we design a FID
(fully interpolation decoding) module that directly upsamples the
multi-resolution feature maps using bilinear interpolation. Inspired by the 3D
distance interpolation used in PointNet++, we argue this FID module is a 2D
version distance interpolation on $(\theta, \phi)$ space. As a parameter-free
decoding module, the FID largely reduces the model complexity by maintaining
good performance. Besides the network structure, we empirically find that our
model predictions have clear boundaries between different semantic classes.
This makes us rethink whether the widely used K-nearest-neighbor
post-processing is still necessary for our pipeline. Then, we realize the
many-to-one mapping causes the blurring effect that some points are mapped into
the same pixel and share the same label. Therefore, we propose to process those
occluded points by assigning the nearest predicted label to them. This NLA
(nearest label assignment) post-processing step shows a better performance than
KNN with faster inference speed in the ablation study. On the SemanticKITTI
dataset, our pipeline achieves the best performance among all projection-based
methods with $64 \times 2048$ resolution and all point-wise solutions. With a
ResNet-34 as the backbone, both the training and testing of our model can be
finished on a single RTX 2080 Ti with 11G memory. The code is released.
