Wenhao Zuo

This paper addresses the core challenges in the 3D point cloud segmentation of the human auricle region, including the low distinguishability of weak-boundary features, the lack of anatomical priors in existing methods, the tendency of boundary features to be overwhelmed by background noise, and the weakening of supervisory signals. We propose GeoPriors-3DEarSeg, a precise geometric-priors-guided 3D point cloud segmentation network for auricle regions. The network incorporates complementary geometric features from three dimensions, normal vector orientation, local Gaussian curvature, and shape diameter function, to characterize the intrinsic geometric differences at the auricle boundary. A geometric-priors-guided QKV gated attention mechanism is designed to selectively enhance the expression of weak-boundary features. Additionally, we introduce a boundary-aware loss function, NVBLoss, which does not rely on extra annotations to strengthen the supervision of boundary features. The experimental results on a human ear dataset demonstrate that our method achieves a mean intersection over union (mIoU) of 0.9857, outperforms the comparison methods, and precisely segments of weak boundaries in the auricle region. This work provides a technical basis for accurate auricle-region point cloud segmentation and offesr methodological insights for future studies on the weak-boundary segmentation of other medical anatomical structures.

Forensic odontology research has shown that teeth are the hardest structures in the human body and can serve as reliable indicators for individual identification. In this study, we propose a Point Transformer based algorithm called MultiDentFormer, which leverages local-to-global feature fusion to achieve 3D dental biometric recognition. The global features refer to the high-level semantic representations of the full dental arch, capturing the overall structure and arrangement of the teeth, while the local features focus on the detailed geometry of individual teeth. To address the limited capability of the original Point Transformer in capturing local information at the early stages of the network, this study introduces a single-point local feature aggregation module. This module aggregates local geometric information and low-level feature representations into each local center point, thereby enhancing the expressiveness of the extracted features. Additionally, to address the issue of detail information loss during the network downsampling process, which weakens feature representation capability, this paper introduces a multi-scale feature fusion strategy. This strategy effectively integrates the rich detail features from shallow layers with the high-level semantic features extracted from deeper layers, compensating for the information lost during downsampling. Experimental evaluations on a IOS dataset comprising 104 individuals and a CBCT dataset with 51 individuals demonstrate that MultiDentFormer achieves superior recognition performance compared to common algorithms in 3D point cloud classification. The local feature aggregation strategy and multi-scale feature fusion strategy enhance the discriminative ability and classification performance of the 3D point cloud feature representations.