Range-view projection provides an efficient method for transforming 3D LiDAR point clouds into 2D range image representations, enabling effective processing with 2D deep learning models. However, a major challenge in this projection is the many-to-one conflict, where multiple 3D points are mapped onto the same pixel in the range image, requiring a selection strategy. Existing approaches typically retain the point with the smallest depth (closest to the LiDAR), disregarding semantic relevance and object structure, which leads to the loss of important contextual information. In this paper, we extend the depth-based selection rule by incorporating contextual information from both instance centers and class labels, introducing two mechanisms: \textit{Centerness-Aware Projection (CAP)} and \textit{Class-Weighted-Aware Projection (CWAP)}. In CAP, point depths are adjusted according to their distance from the instance center, thereby prioritizing central instance points over noisy boundary and background points. In CWAP, object classes are prioritized through user-defined weights, offering flexibility in the projection strategy. Our evaluations on the SemanticKITTI dataset show that CAP preserves more instance points during projection, achieving up to a 3.1\% mIoU improvement compared to the baseline. Furthermore, CWAP enhances the performance of targeted classes while having a negligible impact on the performance of other classes

翻译：距离视图投影提供了一种将3D激光雷达点云转换为2D距离图像表示的高效方法，使其能够通过2D深度学习模型进行有效处理。然而，该投影面临的主要挑战是多对一冲突问题，即多个3D点被映射到距离图像中的同一像素，这需要制定选择策略。现有方法通常保留深度最小（最接近激光雷达）的点，而忽略了语义相关性和物体结构，导致重要上下文信息的丢失。本文通过融合来自实例中心和类别标签的上下文信息，扩展了基于深度的选择规则，提出了两种机制：\textit{中心感知投影（CAP）}和\textit{类别加权感知投影（CWAP）}。在CAP中，根据点与实例中心的距离调整其深度值，从而优先选择实例中心点而非噪声边界点和背景点。在CWAP中，通过用户定义的权重对物体类别进行优先级排序，为投影策略提供了灵活性。我们在SemanticKITTI数据集上的评估表明，CAP在投影过程中保留了更多实例点，与基线方法相比实现了高达3.1\%的mIoU提升。此外，CWAP在显著提升目标类别性能的同时，对其他类别的性能影响可忽略不计。