论文标题
MOTIONSC：在动态环境中实时语义映射的数据集和网络
MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments
论文作者
论文摘要
这项工作通过创建具有准确且完整的动态场景的新颖户外数据集来解决语义场景完成（SSC）数据中的差距。我们的数据集是由每个时间步骤的随机采样视图形成的，该步骤可监督无需遮挡或痕迹的场景的普遍性。我们通过利用最新的3D深度学习体系结构来通过时间信息来增强SSC，从而创建了最先进的开源网络中的SSC基准，并构建基准实时密集的局部语义映射算法MotionsC。我们的网络表明，在存在动态对象的情况下，提出的数据集可以量化和监督准确的场景完成，这可以导致改进的动态映射算法的发展。所有软件均可在https://github.com/umich-curly/3dmapping上找到。
This work addresses a gap in semantic scene completion (SSC) data by creating a novel outdoor data set with accurate and complete dynamic scenes. Our data set is formed from randomly sampled views of the world at each time step, which supervises generalizability to complete scenes without occlusions or traces. We create SSC baselines from state-of-the-art open source networks and construct a benchmark real-time dense local semantic mapping algorithm, MotionSC, by leveraging recent 3D deep learning architectures to enhance SSC with temporal information. Our network shows that the proposed data set can quantify and supervise accurate scene completion in the presence of dynamic objects, which can lead to the development of improved dynamic mapping algorithms. All software is available at https://github.com/UMich-CURLY/3DMapping.
