Aoralscan3 tooth registration dataset

The constructed Aoralscan3 tooth registration dataset includes 1667 samples for training, 156 samples for validation, and 176 samples for testing. Jaw models are generated from hospital patients by oral scanning. The ground truth of the relative pose of each tooth is generated by adding random jittering to the tooth models. For each tooth, ground truth relative pose information was generated by introducing random jittering to the tooth models. This dataset can be used for point cloud registration. which aligns two point clouds and estimates the relative pose between them. In tooth point cloud registration, the inlier ratio (IR) and registration recall (RR) are used as the evaluation criteria. IR measures the fraction of corrected matches (threshold 0.1 mm) in the inferred correspondence set, and it evaluates the accuracy of the predicted correspondence set. RR measures the fraction of correctly registered point cloud pairs, and it is used to evaluate the quality of the predicted 6D pose.构建的Aoralscan3牙齿配准数据集包含1667个训练样本、156个验证样本及176个测试样本。该数据集的颌骨模型通过对临床患者进行口腔扫描获取。每颗牙齿的相对位姿真值（ground truth）通过对牙齿模型施加随机抖动生成。本数据集可用于点云配准任务——该任务旨在对齐两个点云并估算二者间的相对位姿。在牙齿点云配准任务中，采用内点率（inlier ratio, IR）与配准召回率（registration recall, RR）作为评估指标：内点率用于衡量推断匹配集中符合校正匹配的比例（匹配阈值为0.1毫米），以此评估预测匹配集的精度；配准召回率则用于衡量成功配准的点云对占比，以此评估预测的6D位姿质量。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于牙齿点云配准任务的专用数据集，包含总计1999个样本（训练集1667个、验证集156个、测试集176个），数据来源于医院患者的口腔扫描颌骨模型，并通过添加随机抖动生成牙齿相对姿态真值。它适用于点云配准研究，评估指标包括内点比（IR）和配准召回率（RR），旨在提升牙齿配准的精度和效果。