PrimitiveNet: Primitive Instance Segmentation with Local Primitive Embedding under Adversarial Metric

Abstract

We present PrimitiveNet, a novel approach for high-resolution primitive instance segmentation from point clouds on a large scale. Our key idea is to transform the global segmentation problem into easier local tasks. We train a high-resolution primitive embedding network to predict explicit geometry features and implicit latent features for each point. The embedding is jointly trained with an adversarial network as a primitive discriminator to decide whether points are from the same primitive instance in local neighborhoods. Such local supervision encourages the learned embedding and discriminator to describe local surface properties and robustly distinguish different instances. At inference time, network predictions are followed by a region growing method to finalize the segmentation. Experiments show that our method outperforms existing state-of-the-arts based on mean average precision by a significant margin (46.3%) on ABC dataset [31]. We can process extremely large real scenes covering more than 0.1km2. Ablation studies highlight the contribution of our core designs. Finally, our method can improve geometry processing algorithms to abstract scans as lightweight models. Code and data will be available based on Pytorch1 and Mindspore2.