论文标题
龙卷风网络：多视钻石构成模块的多视图语义分割
TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module
论文作者
论文摘要
点云的语义分割是机器人和自动驾驶的场景理解的关键组成部分。在本文中，我们介绍了龙卷风网络 - 用于3D激光点云语义分割的神经网络。我们将多视图（鸟眼和范围）的投影特征提取与编码器解码器重新连接体系结构和新颖的钻石上下文块结合在一起。当前基于投影的方法没有考虑到相邻点通常属于同一类。为了更好地利用这些本地邻里信息并减少嘈杂的预测，我们引入了总变化，lovasz-softmax和加权跨透明拷贝损失的组合。我们还利用了LiDAR数据包含360度视野并使用圆形填充的事实。我们证明了Semantickitti数据集的最新结果，还提供了彻底的定量评估和消融结果。
Semantic segmentation of point clouds is a key component of scene understanding for robotics and autonomous driving. In this paper, we introduce TORNADO-Net - a neural network for 3D LiDAR point cloud semantic segmentation. We incorporate a multi-view (bird-eye and range) projection feature extraction with an encoder-decoder ResNet architecture with a novel diamond context block. Current projection-based methods do not take into account that neighboring points usually belong to the same class. To better utilize this local neighbourhood information and reduce noisy predictions, we introduce a combination of Total Variation, Lovasz-Softmax, and Weighted Cross-Entropy losses. We also take advantage of the fact that the LiDAR data encompasses 360 degrees field of view and uses circular padding. We demonstrate state-of-the-art results on the SemanticKITTI dataset and also provide thorough quantitative evaluations and ablation results.
