acusim: a synthetic dataset for cervicocranial acupuncture points localisation

The locations of acupuncture points (acupoints) differ among human individuals due to variations in factors such as height, weight, and fat proportions. However, acupoint annotation is expert-dependent, labour-intensive, and highly expensive, which limits the data size and detection accuracy. In this paper, we introduce the \"AcuSim\" dataset as a new synthetic dataset for the task of localising points on the human cervicocranial area from an input image using an automatic render and labelling pipeline during acupuncture treatment. It includes the creation of 63,936 RGB-D images and 504 synthetic anatomical models with 174 volumetric acupoints annotated, to capture the variability and diversity of human anatomies. The study validates a convolutional neural network (CNN) on the proposed dataset with an accuracy of 99.73% and shows that 92.86% of predictions in the validation set align within a 5mm threshold of margin error when compared to expert-annotated data. This dataset addresses the ..., , , # AcuSim: A Synthetic Dataset for Cervicocranial Acupuncture Points Localisation

Dryad DOI: https://doi.org/10.5061/dryad.zs7h44jkz

## Dataset Overview

A multi-view acupuncture point dataset containing:

* 64x64, 128x128, 256x256, 512×512 and 1024x1024resolution RGB images
* Corresponding JSON annotations with:
* 2D/3D keypoint coordinates
* Visibility weights (0.9-1.0 scale)
* Meridian category indices
* Visibility masks
* 174 standard acupuncture points (map.txt)
* Occlusion handling implementation

## Key Features

* **Multi-view Rendering**: Generated using Blender 3.5 with realistic occlusion simulation
* **Structured Annotation**:
* Default initialization for occluded points ([0.0, 0.0, 0.5])
* Meridian category preservation for occluded points
* Weighted visibility scoring
* **ML-Ready Format**: Preconfigured PyTorch DataLoader implementation

## Dataset Structure

```
dataset_root/
├── map.txt # Complete list of 174 acupuncture points
├── train/
...,

由于身高、体重、脂肪比例等因素的个体差异，人体穴位（acupoints）的位置存在差异。然而穴位标注依赖专业人员，不仅耗时耗力且成本高昂，这限制了数据集规模与检测精度。本文提出"AcuSim"数据集，这是一款全新的合成数据集，用于针灸治疗场景下基于输入图像实现人体颈颅区域的穴位定位任务，通过自动化渲染与标注流水线完成构建。该数据集包含63936张RGB-D图像与504个合成解剖模型，共标注了174个体积化穴位，以覆盖人体解剖结构的变异性与多样性。本研究在该数据集上验证了卷积神经网络（convolutional neural network, CNN）的性能，其准确率达99.73%；且相较于专家标注数据，验证集中92.86%的预测结果的误差处于5mm的阈值范围内。该数据集旨在解决……
# AcuSim：一款用于颈颅穴位定位的合成数据集

Dryad数字对象标识符：https://doi.org/10.5061/dryad.zs7h44jkz

## 数据集概览

多视角穴位数据集包含以下内容：
* 64×64、128×128、256×256、512×512及1024×1024分辨率的RGB图像
* 配套的JSON标注文件，包含：
* 2D/3D关键点坐标
* 可见性权重（取值范围0.9~1.0）
* 经络类别索引
* 可见性掩码
* 174个标准穴位（详见map.txt）
* 遮挡处理实现模块

## 核心特性

* **多视角渲染**：基于Blender 3.5生成，支持逼真的遮挡模拟
* **结构化标注**：
* 对被遮挡点的默认初始化值为[0.0, 0.0, 0.5]
* 保留被遮挡点的经络类别信息
* 加权可见性评分机制
* **适配机器学习格式**：内置预配置的PyTorch DataLoader实现

## 数据集结构

dataset_root/
├── map.txt # 174个标准穴位的完整列表
├── train/
...,