LiDAR has been widely applied in autonomous driving and mobile robotics. Recently, many studies focus on real-time point cloud segmentation, aiming to achieve higher accuracy while maintaining real-time inference speed. Current real-time methods mostly rely on 2D projection, which inevitably leads to spatial information loss. To address the limitations of 2D projection methods, we propose a Geometry-Enhanced Network called GENet that exploits spatial priors. The network employs an Atrous Separable Range Attention (ASRA) module to explicitly utilize spatial priors from range images, enabling geometry-aware feature aggregation with large receptive field at linear complexity. A Geometry-Context Modulation (GCM) mechanism is then used to calibrate semantic features, incorporating geometric priors while preserving the discriminative ability of original features across different categories. Experiments show that our method achieves efficient information fusion while maintaining real-time performance. Compared to existing methods, GENet requires fewer parameters and less computation, achieving a favorable balance between accuracy and efficiency.
Wu et al. (Thu,) studied this question.