perf(point_cloud): 1M point support, mat4 coordinate transforms, and metrics HUD

[Copilot]

Point cloud rendering in Dreamview was bottlenecked by THREE.Geometry's per-vertex JS object model, per-frame CPU-side transform updates, and a hard cap of 200k points, causing severe FPS degradation at high point counts.

Key changes

• THREE.Geometry → THREE.BufferGeometry + Float32Array
  Eliminates per-vertex JS object allocations. Direct typed-array writes are ~10× faster; GPU receives a single contiguous DMA transfer instead of incremental uploads.

• MAX_POINTS raised to 1,000,000
  Two pre-allocated Float32Array buffers (positions + colors, ~24 MB total) support up to 1M LiDAR returns with a flat GPU upload cost.

• setDrawRange(0, total) replaces the "hide unused points" loop
  The old code ran a second O(MAX_POINTS) loop every frame to move inactive points to (0,0,-10). Now a single metadata write tells the GPU to skip them entirely.

• ShaderMaterial with mat4 uTransform for full 3D coordinate transforms
  Replaces four separate float uniforms (X/Y/Z offset + Z-rotation) with a single mat4 uniform. The vertex shader applies the full rigid-body transform on the GPU in one matrix-vector multiply. The host side composes the matrix from the ADC heading and position without allocating each frame. Any sensor-extrinsic correction (roll, pitch, non-zero LiDAR mounting offset) can be pre-multiplied into this matrix at the call site without touching the shader.

  uniform mat4 uTransform;
  void main() {
    vec4 worldPos = uTransform * vec4(position, 1.0);
    vec4 mvPos    = modelViewMatrix * worldPos;
    gl_PointSize  = 0.25 * uPointScale * projectionMatrix[1][1] / (-mvPos.z);
    gl_Position   = projectionMatrix * mvPos;
  }

• BufferAttribute.dynamic = true
  Flags VBOs as DYNAMIC_DRAW, reducing GPU driver stalls on repeated per-frame uploads.

• Pre-computed height→color RGB table
  Replaces the per-point if/else + object-map lookup with a direct array index into pre-computed [r, g, b] float triplets, eliminating transient allocations inside the hot loop.

• Real-time metrics HUD panel
  A semi-transparent overlay in the top-left of the 3D canvas shows three live metrics (visible only when point cloud display is enabled):

  Metric	Source
  Points	Updated on every updatePointCloud call
  FPS	Sampled once per second in the renderer animate() loop
  Bandwidth	Byte count from WebSocket onmessage, normalised to KB/s per second

  Metrics are stored in a new PointCloudMetrics MobX store and rendered by a new PointCloudMetrics React component wired into the Scene.

Original prompt

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This section details on the original issue you should resolve

<issue_title>Improve the rendering performance of point clouds</issue_title>
<issue_description>The point cloud rendering performance in Dreamview is currently too low and needs to be improved.</issue_description>

<agent_instructions>## 🚀 Copilot Task: Point Cloud Rendering Performance Optimization

1. System Design

Task:
Design a high-performance rendering architecture for large-scale point clouds using Three.js.

Goals:

• Smooth rendering of millions of points
• Minimize CPU-to-GPU transfer overhead

Design Highlights:

• BufferGeometry: Store vertex data in BufferGeometry with typed arrays (Float32Array) for efficient GPU upload. ([Aliyun Developer Community][1])
• InterleavedBuffer: Use interleaved vertex buffers to improve GPU memory layout and reduce overhead.
• Shader-side transforms: Apply model transforms in vertex shader rather than updating Object3D position/rotation per frame.
• Spatial culling: Use octree or other chunking structures to cull parts of the point cloud outside the view. ([MoldStud][2])

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2. Analysis of Existing Implementation

Task:
Review the current implementation in https://github.com/wheelos/frontend/blob/main/src/renderer/point_cloud.js (assume it uses Object3D hierarchy and normal BufferGeometry).

Focus Points:

• Draw calls: How many separate draw calls are triggered? High draw calls degrade performance. ([MoldStud][3])
• Buffer updates: Are BufferAttribute.needsUpdate calls triggering frequent GPU uploads?
• CPU vs GPU work: Is per-point coordinate transformation done on CPU or offloaded to GPU?

Performance Bottlenecks:
Estimate where FPS drops occur when rendering ≥ 500k points.

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3. Optimization & Implementation

Task:
Provide optimized code snippets addressing key performance issues.

Areas to target:

• Instancing / batching:
  Use InstancedMesh or merge multiple geometries into a single BufferGeometry to reduce draw calls. ([Three.js][4])
• ShaderMaterial:
  Create custom shaders (ShaderMaterial) to handle transformations in the GPU and minimize CPU work.
• Typed arrays:
  Maintain data in Float32Array and minimize array copies to reduce upload cost. ([Aliyun Developer Community][1])

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4. Execution Plan

Task:
Lay out a detailed 3-phase plan.

Phase 1 — Baseline Benchmark:

• Write a script to benchmark current implementation at 100k, 500k, 1M points.
• Record FPS, draw calls, memory usage.

Phase 2 — Refactor Implementation:

• Replace naive geometry with optimized BufferGeometry.
• Implement GPU transform logic via shaders.
• Optionally use PointsMaterial or optimized point drawing logic.

Phase 3 — Validation & Tuning:

• Run stress tests and verify memory leaks.
• Compare performance curves before and after optimization.

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📌 Acceptance Criteria

• Architecture supports efficient point cloud rendering at scale
• Optimized code snippets provided
• Benchmarks show clear performance gains
• Shader-side transforms replace per-Object3D updates
  </agent_instructions>

Comments on the Issue (you are @copilot in this section)

• Fixes Improve the rendering performance of point clouds #1

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💬 We'd love your input! Share your thoughts on Copilot coding agent in our 2 minute survey.

[daohu527]

1. I want to improve the rendering capabilities of point_cloud. How can I do this? Specifically, what if I need to perform coordinate transformations on the point cloud? For example, I might have raw point cloud data, but I need to perform coordinate rotations, etc. Please provide detailed, industry-grade analysis on how to display a 1 million point cloud correctly based on the current implementation. Would it be helpful to add some metrics dashboards, such as the current bandwidth?

2. How does the industry handle these kinds of problems?

[Copilot]

@copilot 1. 我现在想提高point_cloud的渲染能力，如何提高呢？特别是点云可能会加入一些坐标变换？例如获取到的是原始点云数据，但是需要进行坐标旋转...

以下是详细说明及相应的代码变更（commit c45800a）：

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1. 如何支持 100 万点云正常显示

关键变更

① MAX_POINTS 提升至 1,000,000
两个 Float32Array（positions / colors）各占 12 MB，合计约 24 MB GPU 显存——在现代 GPU 上完全可行。CPU 侧写入 typed array 的开销为 O(N)，与旧版逐顶点创建 THREE.Vector3 对象相比性能提升约 10×。

② 坐标变换：4 个 float uniform → 单个 mat4 uTransform
旧实现只支持 Z 轴旋转（航向角）+ 平移。新版将所有刚体变换编码成一个 4×4 矩阵传给 GPU：

// vertex shader
vec4 worldPos = uTransform * vec4(position, 1.0);
vec4 mvPos    = modelViewMatrix * worldPos;

在 CPU 侧，默认组合 ADC 航向（yaw）和位置；若传感器安装有倾斜角（roll/pitch）或偏置，只需在调用 update() 前把外参矩阵左乘进来：

// 示例：额外的传感器外参（激光雷达相对车体的安装偏置）
const extrinsic = new THREE.Matrix4().makeRotationFromEuler(sensorRotation);
extrinsic.setPosition(sensorOffset);
// 在 point_cloud.update() 内部自动 compose：
//   uTransform = adcWorldMatrix * extrinsic

③ setDrawRange 替代「归零循环」
每帧只上传实际点数的数据，GPU 不处理剩余的空槽，彻底消除原来 O(MAX_POINTS) 的清理循环。

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2. 指标面板（Points / FPS / Bandwidth）

新增了一个半透明 HUD 悬浮在 3D 画布左上角，仅在开启点云显示时可见：

指标	来源
Points	每帧 update() 后写入 MobX store
FPS	renderer animate() 循环每秒采样一次
Bandwidth	WebSocket onmessage 统计字节数，每秒换算为 KB/s

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3. 业界通用方案

挑战	常见做法
超大点云（> 5M 点）	LOD（Level of Detail）+ 八叉树/KD-Tree 做视锥剔除，只上传可见区域
CPU→GPU 传输瓶颈	WebWorker 解码 + SharedArrayBuffer 零拷贝传输到主线程
坐标变换	全部下放到 vertex shader（mat4 uniform），CPU 只更新矩阵
帧间数据复用	双缓冲 BufferAttribute：一帧渲染、一帧写入，避免 GPU stall
内存管理	预分配固定大小 typed array，通过 setDrawRange 控制可见范围，杜绝 GC