论文标题
置换晶格上的时间语义分割的抽象流量
Abstract Flow for Temporal Semantic Segmentation on the Permutohedral Lattice
论文作者
论文摘要
语义分割是自主代理要求的核心能力，因为能够区分场景的哪个部分属于对象类对于导航和与环境的相互作用至关重要的。仅使用一个时间阶段数据的方法无法区分移动对象，也无法从时间集成中受益。在这项工作中，我们扩展了一个骨干晶格以处理时间点云数据。此外，我们从光流方法中汲取灵感，并提出了一个称为“抽象流”的新模块，该模块允许网络与场景的各个部分匹配相似的抽象功能并在时间上收集信息。我们在Semantickitti数据集中获得了最新的结果，该数据集包含来自真实城市环境的激光扫描。我们在https://github.com/ais-bonn/temporal_latticenet上共享turemallatticeNet的pytorch实现。
Semantic segmentation is a core ability required by autonomous agents, as being able to distinguish which parts of the scene belong to which object class is crucial for navigation and interaction with the environment. Approaches which use only one time-step of data cannot distinguish between moving objects nor can they benefit from temporal integration. In this work, we extend a backbone LatticeNet to process temporal point cloud data. Additionally, we take inspiration from optical flow methods and propose a new module called Abstract Flow which allows the network to match parts of the scene with similar abstract features and gather the information temporally. We obtain state-of-the-art results on the SemanticKITTI dataset that contains LiDAR scans from real urban environments. We share the PyTorch implementation of TemporalLatticeNet at https://github.com/AIS-Bonn/temporal_latticenet .
