Indoor 3D referring segmentation aims to identify and segment the target object in a point cloud according to a natural language expression. Although recent advances in multimodal feature fusion have markedly improved this task, existing methods do not jointly model the structural regularities commonly observed in indoor objects, such as approximate symmetry and repetitive local patterns, in conjunction with the directional constraints conveyed by referring expressions. In this work, we formulate symmetry-aware representation as the extraction of direction-consistent structural responses from both forward and backward traversals of the same spatially ordered sequence while preserving direction-sensitive variations introduced by occlusion, point cloud incompleteness, and cluttered scene layouts. Based on this formulation, we propose PCMTRefer, a symmetry-aware text-guided Point Mamba framework for indoor 3D referring segmentation. The input point cloud is first partitioned using an octree and arranged into a spatially coherent sequence via Z-order (Morton) ordering. A bidirectional state-space encoder then aggregates complementary context from both traversal directions, yielding richer representations of regular boundaries, repetitive structures, and approximately bilateral object geometries. In parallel, an asymmetric text-to-point guidance module injects semantic cues—object categories, attributes, and spatial relationships—into point-wise features, while a Background-Relaxation Token offers an auxiliary matching channel for non-target background regions. A Gumbel-Softmax-based semantic primitive learning module further extracts discriminative cues from referring expressions and integrates language semantics with point-level geometric features through a multi-scale decoder. Experimental results on the ScanRefer benchmark show that PCMTRefer achieves an Overall Acc@0.25 of 58.56%, an Overall Acc@0.5 of 54.19%, and an mIoU of 49.97%. Multi-seed validation further confirms the statistical stability of these results, with standard deviations of less than 0.2% across three independent runs.

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.