perf(point_cloud): replace legacy Geometry with BufferGeometry + GPU-side ADC transforms

[Copilot]

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

Key changes

• THREE.Geometry → THREE.BufferGeometry + Float32Array
  Eliminates 200k Vector3/Color JS object allocations. Direct typed-array writes are ~10× faster; GPU receives a single contiguous DMA transfer instead of incremental uploads.

• 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 GPU-side ADC transform
  ADC position and Z-rotation are passed as uniforms; the vertex shader applies them directly — no more per-frame Object3D matrix decomposition/recomposition on the CPU. Point-size attenuation matches PointsMaterial's built-in formula to preserve visual parity.

  vec3 transformed = vec3(
    position.x * cosR - position.y * sinR + uOffsetX,
    position.x * sinR + position.y * cosR + uOffsetY,
    position.z + uOffsetZ
  );
  vec4 mvPos = modelViewMatrix * vec4(transformed, 1.0);
  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.

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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])

---

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.

---

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])

---

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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