Add detailed references and notes for 3D multi-view human pose estimation systems in README

README.md

@@ -3,10 +3,86 @@
 调研现有的多视角单人 (multiple view of single person) 多视角多人 (multi-view multi-person)
 人体姿态估计系统/管线
 
+## 参考系统
+
+### [EasyMocap](https://chingswy.github.io/easymocap-public-doc/)
+
+- Github: [zju3dv/EasyMocap](https://github.com/zju3dv/EasyMocap)
+- IK fitting [easymocap/multistage/fitting.py](https://github.com/zju3dv/EasyMocap/blob/master/easymocap/multistage/fitting.py)
+- Pose Prior [easymocap/multistage/gmm.py](https://github.com/zju3dv/EasyMocap/blob/master/easymocap/multistage/gmm.py)
+- Tracking: [Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views](https://arxiv.org/abs/1901.04111)
+    - implementation: [easymocap/assignment/track.py](https://github.com/zju3dv/EasyMocap/blob/master/easymocap/assignment/track.py#L199) note that assignment is implemented in C++ (matchSVT, or [matchSVT.hpp](https://github.com/zju3dv/EasyMocap/blob/master/library/pymatch/include/matchSVT.hpp))
+    - demo page: [mvmp.md](https://github.com/zju3dv/EasyMocap/blob/master/doc/mvmp.md) or [04 multiperson](https://chingswy.github.io/easymocap-public-doc/develop/04_multiperson.html)
+- Visualization: [Open3D](https://www.open3d.org/) (see [apps/vis/vis_client.py](https://github.com/zju3dv/EasyMocap/blob/master/apps/vis/vis_client.py), a very boring TCP naive socket client-server architecture)
+    - see also [doc/realtime_visualization.md](https://github.com/zju3dv/EasyMocap/blob/master/doc/realtime_visualization.md)
+
+> [!NOTE]
+> 它也是有其显然的问题的, 找出其问题所在可作为各位同学的练习
+
+### [FreeMocap](https://freemocap.org)
+
+> Free Motion Capture for Everyone 
+
+- Github: [freemocap/freemocap](https://github.com/freemocap/freemocap)
+- YouTube: [@jonmatthis](https://www.youtube.com/@jonmatthis) 似乎是大学讲师, 有分享 lectures
+
+个人项目, 规模不大
+
+### [OpenSim](https://opensim.stanford.edu) 系列
+
+斯坦福大学的项目, 大抵的架构能力是比我强的, 但是目标和我试图达到的不同
+
+#### [Pose2Sim](https://github.com/perfanalytics/pose2sim)
+
+> Pose2Sim stands for "OpenPose to OpenSim", as it originally used OpenPose inputs (2D keypoints coordinates) and led to an OpenSim result (full-body 3D joint angles). 
+
+- Camera synchronization
+- Multi-person identification
+- Robust triangulation
+- 3D coordinates filtering
+- Marker augmentation
+
+#### [OpenCap](https://www.opencap.ai)
+
+和 [Pose2Sim](https://github.com/perfanalytics/pose2sim) 试图做的事情似乎类似, 但是给的管线是基于两台
+iPhone 便可实现的, 更加成熟
+ 
+#### [Sports2D](https://github.com/davidpagnon/Sports2D)
+
+和 [Splyza Motion](https://motion.products.splyza.com) 做的事情类似
+
+单视角
+
+> [!IMPORTANT]
+> note that the motion must lie in the sagittal or frontal plane.
+>
+> 应该只进行一次深度估计; 也就是说朝着/远离相机的视线方向的运动是无法估计的
+
+> [!NOTE]
+> 注意, 只能是单相机, 否则没有相机外参, 或者说不知道原点在哪里; 而单相机实现如
+[SMPLify](https://smplify.is.tue.mpg.de) 类似的实现只需要估计深度
+
+## 注意
+
+devil is in the details
+
+每一个数据流, 其 source 从哪里来, 以什么格式 (tensor? image? encoded video? byte stream?) 传输,
+传输的协议 (socket? shared memory? file IO?) 是什么, 传输的频率是多少 (等间隔? 若等间隔, 采样频率是多少 Hz? 不等间隔时间戳哪里来的? 以什么为时基?) 预估的带宽是多少? (这又决定了传输协议的选择)
+
+必定有 source 与 sink, 还需要填充中间的部分 (能填充得越详细越好)
+
+是单个进程还是多个进程? (答案应该是显然的) 能否分布式? (边缘计算的扩展可能)
+
+表示层往往是 sink, 而渲染 3D 表示往往是 (游戏) 引擎等价的工作量
+
+可以用 LLM, 但是能否分辨祂是否在胡说八道?
+
 ## 参考答案
 
 AI 生成的架构图, 不代表我此刻的实际想法, which changes every moment.
 
+能把每一个横线的细节都填上嘛? (确实有许多纰漏)
+
 ```mermaid
 flowchart TD
   %% =========================