论文标题
空间语义嵌入网络：快速3D实例分段，深度度量学习
Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning
论文作者
论文摘要
我们提出了空间语义嵌入网络（SSEN），这是一种使用深度度量学习的简单但有效的算法，用于3D实例分割。室内环境的原始3D重建遭受了阻塞，噪声，并且在单个实体之间没有任何有意义的区别。对于大规模场景中的高级智能任务，3D实例分割识别对象的各个实例。我们通过简单地学习正确的嵌入空间来处理实例分割，从而将对象的单个实例映射到反映空间和语义信息的不同群集中。与以前需要复杂的预处理或后处理的方法不同，我们的实施是紧凑且具有竞争性能的速度，可以在具有高分辨率体素的大型场景上保持可扩展性。我们在AP得分上的扫描3D实例分段基准中演示了我们的算法的最先进性能。
We propose spatial semantic embedding network (SSEN), a simple, yet efficient algorithm for 3D instance segmentation using deep metric learning. The raw 3D reconstruction of an indoor environment suffers from occlusions, noise, and is produced without any meaningful distinction between individual entities. For high-level intelligent tasks from a large scale scene, 3D instance segmentation recognizes individual instances of objects. We approach the instance segmentation by simply learning the correct embedding space that maps individual instances of objects into distinct clusters that reflect both spatial and semantic information. Unlike previous approaches that require complex pre-processing or post-processing, our implementation is compact and fast with competitive performance, maintaining scalability on large scenes with high resolution voxels. We demonstrate the state-of-the-art performance of our algorithm in the ScanNet 3D instance segmentation benchmark on AP score.
