  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.
