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README.md

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+# Homework 01: Survey & Design of 3D Multi-view Human Pose Estimation System
+
+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
+
+## 参考答案
+
+AI 生成的架构图, 不代表我此刻的实际想法, which changes every moment.
+
+```mermaid
+flowchart TD
+  %% =========================
+  %% Multi-view 2D stage
+  %% =========================
+  subgraph VIEWS["Per-view input (cameras 1..N)"]
+    direction LR
+    C1["Cam 1\n2D detections: 133×2 (+conf)"] --> T1["2D latest tracking cache\n(view 1)"]
+    C2["Cam 2\n2D detections: 133×2 (+conf)"] --> T2["2D latest tracking cache\n(view 2)"]
+    C3["Cam 3\n2D detections: 133×2 (+conf)"] --> T3["2D latest tracking cache\n(view 3)"]
+    C4["Cam 4\n2D detections: 133×2 (+conf)"] --> T4["2D latest tracking cache\n(view 4)"]
+  end
+
+  %% =========================
+  %% Cross-view association
+  %% =========================
+  subgraph ASSOC["Cross-view data association (epipolar)"]
+    direction TB
+    EPI["Epipolar constraint\n(Sampson / point-to-epiline)"]:::core
+    CYCLE["Cycle consistency / view-graph pruning"]:::core
+    GROUP["Assemble per-target multi-view observation set\n{view_id → 133×2}"]:::core
+    EPI --> CYCLE --> GROUP
+  end
+
+  T1 --> EPI
+  T2 --> EPI
+  T3 --> EPI
+  T4 --> EPI
+
+  %% =========================
+  %% Geometry / lifting
+  %% =========================
+  subgraph GEOM["3D measurement construction"]
+    direction TB
+    RT["Camera models\nK, [R|t], SO(3)/SE(3)"]:::meta
+    DLT["DLT / triangulation (init)"]:::core
+    NN["Optional NN lifting / completion"]:::core
+    BA["Optional reprojection refinement\n(1–5 iters)"]:::core
+    Y["3D measurement y(t)\nJ×3 positions (+quality / R / cov)"]:::out
+    RT --> DLT --> NN --> BA --> Y
+  end
+
+  GROUP --> DLT
+
+  %% =========================
+  %% Tracking filter + lifecycle
+  %% =========================
+  subgraph FILTER["Tracking filter (per target)"]
+    direction TB
+    GATE["Gating\n(Mahalanobis / per-joint + global)"]:::core
+    IMM["IMM (motion model bank)\n(CV/CA or low/med/high Q)"]:::core
+    PRED["Predict\nΔt, self-propagate"]:::core
+    UPD["Update\nKF (linear)\nstate: [p(3J), v(3J)]"]:::core
+    MISS["Miss handling & track lifecycle\n(tentative → confirmed → deleted)"]:::meta
+
+    GATE --> IMM --> PRED --> UPD --> MISS
+  end
+
+  Y --> GATE
+
+  %% Optional inertial fusion
+  IMU["IMU (optional)"]:::meta --> INERT["EKF/UKF branch (optional)\nwhen augmenting state with orientation"]:::meta --> IMM
+
+  %% =========================
+  %% IK + optional feedback
+  %% =========================
+  subgraph IKSTAGE["IK stage (constraint / anatomy)"]
+    direction TB
+    IK["IK optimization target\n(minimize joint position error,\nadd bone length / joint limits)"]:::core
+    FB["Optional feedback to filter\npseudo-measurement z_IK with large R"]:::meta
+    IK --> FB
+  end
+
+  UPD --> IK
+  FB -.-> GATE
+
+  %% =========================
+  %% SMPL / mesh fitting
+  %% =========================
+  subgraph SMPLSTAGE["SMPL / SMPL-X fitting"]
+    direction TB
+    VP["VPoser / pose prior"]:::core
+    SMPL["SMPL(θ, β, root)\nfit to joints / reprojection"]:::core
+    JR["JR: Joint Regressor\nmesh → joints (loop closure)"]:::core
+    OUT["Outputs\nmesh + joints + pose params"]:::out
+    VP --> SMPL --> JR --> OUT
+    JR -. residual / reproject .-> SMPL
+  end
+
+  IK --> SMPL
+
+  classDef core fill:#0b1020,stroke:#5eead4,color:#e5e7eb,stroke-width:1.2px;
+  classDef meta fill:#111827,stroke:#93c5fd,color:#e5e7eb,stroke-dasharray: 4 3;
+  classDef out fill:#052e2b,stroke:#34d399,color:#ecfeff,stroke-width:1.4px;
+```
+
+如果图片不能正确预览, 见 [fig/fig.svg](fig/fig.svg)