提出深度感知模块，让点云分割模型更好利用范围图中的深度信息。
Depth-Aware Range Image-Based Model for Point Cloud Segmentation
- 设计深度感知模块，显式建模范围图中通道间的深度依赖关系。
- 在SemanticKITTI等数据集上显著提升分割精度，计算开销几乎为零。
- 适合需要高效高精度点云分割的自动驾驶与机器人场景。
点云分割（PCS）旨在将点划分为不同且有意义的组，在机器人领域对环境理解至关重要。为实时处理稀疏且大规模的室外点云，范围图像基模型被广泛采用。然而，范围图像缺乏显式深度信息，导致三维空间中分离的物体在图像中接触，增加了分割难度。此外，现有PCS模型多源于彩色图像模型，未能充分利用范围图像中隐含但有序的深度信息，性能受限。本文提出深度感知模块（DAM），通过显式建模通道间依赖关系，感知范围图像中的有序深度信息。Fast FMVNet V3将DAM集成至每个架构阶段的最后块中。在SemanticKITTI、nuScenes和SemanticPOSS上的大量实验表明，DAM显著提升了Fast FMVNet V3性能，且计算开销可忽略不计。
原文摘要 · Abstract (English)
Point cloud segmentation (PCS) aims to separate points into different and meaningful groups. The task plays an important role in robotics because PCS enables robots to understand their physical environments directly. To process sparse and large-scale outdoor point clouds in real time, range image-based models are commonly adopted. However, in a range image, the lack of explicit depth information inevitably causes some separate objects in 3D space to touch each other, bringing difficulty for the range image-based models in correctly segmenting the objects. Moreover, previous PCS models are usually derived from the existing color image-based models and unable to make full use of the implicit but ordered depth information inherent in the range image, thereby achieving inferior performance. In this paper, we propose Depth-Aware Module (DAM) and Fast FMVNet V3. DAM perceives the ordered depth information in the range image by explicitly modelling the interdependence among channels. Fast FMVNet V3 incorporates DAM by integrating it into the last block in each architecture stage. Extensive experiments conducted on SemanticKITTI, nuScenes, and SemanticPOSS demonstrate that DAM brings a significant improvement for Fast FMVNet V3 with negligible computational cost.
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