  Point cloud segmentation (PCS) plays an essential role in robot perception
and navigation tasks. To efficiently understand large-scale outdoor point
clouds, their range image representation is commonly adopted. This image-like
representation is compact and structured, making range image-based PCS models
practical. However, undesirable missing values in the range images damage the
shapes and patterns of objects. This problem creates difficulty for the models
in learning coherent and complete geometric information from the objects.
Consequently, the PCS models only achieve inferior performance. Delving deeply
into this issue, we find that the use of unreasonable projection approaches and
deskewing scans mainly leads to unwanted missing values in the range images.
Besides, almost all previous works fail to consider filling in the unexpected
missing values in the PCS task. To alleviate this problem, we first propose a
new projection method, namely scan unfolding++ (SU++), to avoid massive missing
values in the generated range images. Then, we introduce a simple yet effective
approach, namely range-dependent $K$-nearest neighbor interpolation ($K$NNI),
to further fill in missing values. Finally, we introduce the Filling Missing
Values Network (FMVNet) and Fast FMVNet. Extensive experimental results on
SemanticKITTI, SemanticPOSS, and nuScenes datasets demonstrate that by
employing the proposed SU++ and $K$NNI, existing range image-based PCS models
consistently achieve better performance than the baseline models. Besides, both
FMVNet and Fast FMVNet achieve state-of-the-art performance in terms of the
speed-accuracy trade-off. The proposed methods can be applied to other range
image-based tasks and practical applications.
