Motivated by how humans perceive geometry and color to recognize objects, we propose a boundary embedded attentional convolution (BEACon) network for point cloud instance segmentation. At the core of BEACon, we introduce the attentional weight in the convolution layer to adjust the neighboring features, with the weight being adapted to the relationship between geometry and color changes. As a result, BEACon makes use of both geometry and color information, takes instance boundary as an important feature, and thus learns a more discriminative feature representation in the neighborhood. Experimental results show that BEACon outperforms the state-of-the-art by a large margin. Ablation studies are also provided to prove the large benefit of incorporating both geometry and color into attention weight for instance segmentation.

受人类如何感知几何形状和颜色以识别对象的启发，我们提出了一种用于点云实例分割的边界嵌入式注意力卷积（BEACon）网络。在BEACon的核心中，我们在卷积层中引入注意权重以调整相邻特征，权重适用于几何形状和颜色变化之间的关系。结果，BEACon同时利用了几何和颜色信息，将实例边界作为重要特征，从而在附近学习了更具判别力的特征表示。实验结果表明，BEACon在很大程度上优于最新技术。还提供了消融研究，以证明将几何形状和颜色都纳入注意力权重（例如细分）的巨大好处。