This paper proposes EyeNet, a novel semantic segmentation network for point clouds that addresses the critical yet often overlooked parameter of coverage area size. Inspired by human peripheral vision, EyeNet overcomes the limitations of conventional networks by introducing a simple but efficient multi-contour input and a parallel processing network with connection blocks between parallel streams. The proposed approach effectively addresses the challenges of dense point clouds, as demonstrated by our ablation studies and state-of-the-art performance on Large-Scale Outdoor datasets.

翻译：本文提出了一种新颖的点云语义分割网络EyeNet，该网络解决了关键但常被忽视的覆盖区域大小参数问题。受人类周边视觉启发，EyeNet通过引入简单高效的多轮廓输入机制及带连接块的并行处理网络架构，克服了传统网络的局限性。所提方法有效解决了密集点云带来的挑战，消融实验及在大规模户外数据集上取得的最优性能验证了其有效性。