Current 3D instance segmentation models generally use multi-stage methods to extract instance objects, including clustering, feature extraction, and post-processing processes. However, these multi-stage approaches rely on hyperparameter settings and hand-crafted processes, which restrict the inference speed of the model. In this paper, we propose a new 3D point cloud instance segmentation network, named OSIS. OSIS is a one-stage network, which directly segments instances from 3D point cloud data using neural network. To segment instances directly from the network, we propose an instance decoder, which decodes instance features from the network into instance segments. Our proposed OSIS realizes the end-to-end training by bipartite matching, therefore, our network does not require computationally expensive post-processing steps such as non maximum suppression (NMS) and clustering during inference. The results show that our network finally achieves excellent performance in the commonly used indoor scene instance segmentation dataset, and the inference speed of our network is only an average of 138ms per scene, which substantially exceeds the previous fastest method.

翻译：当前3D实例分割模型通常采用多阶段方法提取实例对象，包括聚类、特征提取和后处理流程。然而，这些多阶段方法依赖超参数设置和手工设计流程，限制了模型的推理速度。本文提出一种名为OSIS的新型3D点云实例分割网络。OSIS是一种单阶段网络，可直接利用神经网络从3D点云数据中分割出实例对象。为实现网络直接分割实例，我们提出一种实例解码器，将网络中的实例特征解码为实例分割结果。所提出的OSIS通过二分图匹配实现端到端训练，因此该网络在推理过程中无需非极大值抑制（NMS）和聚类等高计算开销的后处理步骤。实验结果表明，本网络在常用的室内场景实例分割数据集上取得了优异性能，且每个场景的平均推理速度仅为138毫秒，显著超越此前最快方法。