# 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)