用点云原生表示，让模型更准地发现3D缺陷。
Back to Point: Exploring Point-Language Models for Zero-Shot 3D Anomaly Detection
- 直接处理点云，不转成图像，保留几何细节。
- 在Real3D-AD和Anomaly-ShapeNet上优于现有方法。
- 适合工业质检中无样本训练的缺陷检测场景。
零样本（ZS）3D异常检测对可靠工业检测至关重要，可在无需目标类别训练数据的情况下检测并定位缺陷。现有方法将3D点云渲染为2D图像，利用预训练视觉语言模型（VLMs）进行检测，但此类策略不可避免地丢失几何细节，且对局部异常敏感度不足。本文重新审视内在3D表示，探索预训练点语言模型（PLMs）在ZS 3D异常检测中的潜力。提出BTP（Back To Point）框架，有效对齐3D点云与文本嵌入。具体而言，BTP将多粒度块特征与文本表示对齐以实现局部异常检测，并引入几何描述符增强对结构异常的敏感性。此外，提出联合表示学习策略，利用辅助点云数据提升鲁棒性并丰富异常语义。在Real3D-AD和Anomaly-ShapeNet上的大量实验表明，BTP在零样本3D异常检测中表现更优。代码将公开于：https://github.com/wistful-8029/BTP-3DAD。
原文摘要 · Abstract (English)
Zero-shot (ZS) 3D anomaly detection is crucial for reliable industrial inspection, as it enables detecting and localizing defects without requiring any target-category training data. Existing approaches render 3D point clouds into 2D images and leverage pre-trained Vision-Language Models (VLMs) for anomaly detection. However, such strategies inevitably discard geometric details and exhibit limited sensitivity to local anomalies. In this paper, we revisit intrinsic 3D representations and explore the potential of pre-trained Point-Language Models (PLMs) for ZS 3D anomaly detection. We propose BTP (Back To Point), a novel framework that effectively aligns 3D point cloud and textual embeddings. Specifically, BTP aligns multi-granularity patch features with textual representations for localized anomaly detection, while incorporating geometric descriptors to enhance sensitivity to structural anomalies. Furthermore, we introduce a joint representation learning strategy that leverages auxiliary point cloud data to improve robustness and enrich anomaly semantics. Extensive experiments on Real3D-AD and Anomaly-ShapeNet demonstrate that BTP achieves superior performance in ZS 3D anomaly detection. Code will be available at \href{https://github.com/wistful-8029/BTP-3DAD}{https://github.com/wistful-8029/BTP-3DAD}.
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