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.

翻译：点云分割（PCS）在机器人感知与导航任务中扮演着关键角色。为高效理解大规模室外点云，通常采用其距离图像表示。这种类图像的表示形式紧凑且结构化，使得基于距离图像的PCS模型具有实用性。然而，距离图像中存在的非期望缺失值会破坏物体的形状与模式。该问题导致模型难以从物体中学习连贯且完整的几何信息，进而使PCS模型仅能获得较差的性能。深入探究此问题，我们发现不合理投影方法的使用及去畸变扫描是导致距离图像中出现非期望缺失值的主要原因。此外，几乎所有先前的研究均未考虑在PCS任务中填补这些意外缺失值。为缓解此问题，我们首先提出一种新的投影方法——扫描展开++（SU++），以避免生成的距离图像中出现大量缺失值。随后，我们引入一种简单而有效的方法——基于距离的$K$近邻插值（$K$NNI），以进一步填补缺失值。最后，我们提出了填补缺失值网络（FMVNet）与快速FMVNet。在SemanticKITTI、SemanticPOSS和nuScenes数据集上的大量实验结果表明，通过采用所提出的SU++与$K$NNI，现有基于距离图像的PCS模型均能持续取得优于基线模型的性能。此外，FMVNet与Fast FMVNet均在速度-精度权衡方面达到了最先进的性能。所提方法可应用于其他基于距离图像的任务及实际应用场景。