3-DFineRec: Fine-Grained Recognition for Small-Scale Objects in 3-D Point Cloud Scenes

Recognizing small objects with fine-grained categories in a cluttered scenario is challenging in some industrial applications. This article proposes a 3-D fine-grained recognition framework for segmenting and classifying small-scale objects with local structural differences from cluttered point clouds. The framework consists of two stages: improved semantic segmentation of 3-D scenarios and fine-grained 3-D instance recognition. In the first stage, normal-angle cues are integrated into discriminative feature learning to enhance local structure representation for small objects. In addition, sampling strategies are used to improve the efficiency of cluttered scenario segmentation. In the second stage, we design a fine-grained classification subnetwork (FGC-Net) based on an attention mechanism that predicts subcategory labels of segmented object instances in metacategories. The experiments were conducted on an aeroengine point cloud collected in the real-world industrial scene. The results demonstrate that the proposed algorithm recognizes small components of multiple fine-grained subcategories with the best scene segmentation mIoU of 88.01% and the overall fine-grained recognition rate of 92.1%. The detailed intermediate result and ablation analysis are also presented. The method has been applied in the actual industrial manufacturing and inspecting field.