For the three-dimensional defect detection task of SOP chip pins, existing point cloud deep learning methods struggle to effectively detect common pin defects. To address this issue, a DCPP image is defined and a corresponding DCPP dataset is created. A DCPP-PointNet defect detection algorithm is also proposed, specifically designed for DCPP images. This algorithm incorporates a LSEF network, which enhances the model’s rotational robustness and ensures good detection performance even with rotated point cloud data. Additionally, a new iRMSC-Net network is designed to replace the feature encoder in PointNet++, improving the model’s ability to learn local edge features of point clouds and enabling precise classification and location of common SOP chip pin defects. Focal loss function is employed to tackle the imbalance between positive and negative samples, allowing the model to focus more on hard-to-distinguish defect samples and thus improving detection accuracy. Experimental results on the self-built DCPP dataset show that the DCPP-PointNet network surpasses existing point cloud segmentation models such as PointNet, PointNet++, and DGCNN in terms of OA and mIoU. It achieved an OA of 98.9% and an mIoU of 93.7%. Ablation studies further confirm the effectiveness of the improvements in DCPP-PointNet, where the combined action of the LSFE network, iRMSC-Net feature encoder, and Focal loss function significantly enhances the model’s detection accuracy and robustness.