基于改进FPCC的实例分割算法

3D点云实例分割是工业自动化中关键的预处理步骤.然而,在工业抓取场景中存在大量遮挡时, 3D点云的实例分割网络通常难以区分相似对象.针对这一问题,本文提出了一种基于FPCC的改进算法.本算法有两个分支:中心点分支,用于推断实例的中心点,以及嵌入式特征分支,用于描述点的特征.分割结果是使用聚类算法获得的.特征增强(FEH)模块对中心点预测的准确性起着至关重要的作用.该模块使用特征增强方法来提高预测的准确性,并且进一步地针对中心点预测而进行了损失函数的修改.实验结果表明,改进后的算法相比于FPCC算法在Precision值和Recall值上分别提高了10%、15%.

Instance Segmentation Algorithm Based on Improved FPCC

Instance segmentation of 3D point clouds is a critical preprocessing step in industrial automation. However, there are often many occlusions in industrial grasping scenarios, which makes it difficult for instance segmentation networks of 3D point clouds to distinguish between similar objects. To this end, this study proposes an improved algorithm based on FPCC. This algorithm has two branches, including a center point branch for inferring the center points of instances and an embedded feature branch for describing point features. The segmentation results are obtained by clustering algorithms. The feature enhancement (FEH) module plays a crucial role in improving the accuracy of center point prediction. This module employs FEH methods to improve the prediction accuracy and further modifies the loss function for center point prediction. Experimental results show that compared with the FPCC algorithm, the improved algorithm increases the Precision and Recall values by 10% and 15% respectively.