feat: LOD 网格分级——meshopt 解析期简化 + 按距离/级联选级
vendored meshoptimizer 1.2（submodule 环境故障改直接 vendor）。Mesh 多 LOD：各级索引连续拼进同一 index buffer（共享顶点，LodRange 区间 选择），解析期 meshopt_simplify 生成链（LOD0=原始，每级目标减半， 相对误差 1e-2，小网格复用上级）；都在 worker 线程，加载零额外卡顿。 SceneRenderer 按 (mesh, LOD) 聚合 batch（lookup key = 指针<<8|lod）： 主视图按屏占比近似 radius/dist 选级（阈值 10/20 倍半径）；阴影级联 按级联序降级（越远越简）。Draw 按 batch.lod 选索引区间。 A/B 对比（100 背包 10×10，RTX 5060 Laptop，vsync off）： - 纯背包：LOD 开 93-95 fps / 关 90-107 fps —— 无显著差异 - 加 SunTemple：LOD 开 52-56 / 关 51-55 —— 无显著差异 结论：当前场景瓶颈不在顶点数（5060 顶点吞吐富余），LOD 基础设施 保留（几何复杂资产/低端 GPU/更大场景生效），帧率问题转向 Tracy GPU 时间线定位真实瓶颈。 坑：LodRange 记录"LOD 不足复用最后一级"时 first 不得继续累加 （buffer 无重复数据，累加必越界——VUID-08798 抓的）。
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import assert from 'assert/strict';
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import { MeshoptClusterizer as clusterizer } from './meshopt_clusterizer.js';
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process.on('unhandledRejection', (error) => {
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console.log('unhandledRejection', error);
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process.exit(1);
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});
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const cubeWithNormals = {
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vertices: new Float32Array([
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// n = (0, 0, 1)
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-1.0, -1.0, 1.0, 0.0, 0.0, 1.0, 1.0, -1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, -1.0, 1.0, 1.0, 0.0, 0.0, 1.0,
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// n = (0, 0, -1)
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-1.0, 1.0, -1.0, 0.0, 0.0, -1.0, 1.0, 1.0, -1.0, 0.0, 0.0, -1.0, 1.0, -1.0, -1.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0, -1.0,
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// n = (1, 0, 0)
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1.0, -1.0, -1.0, 1.0, 0.0, 0.0, 1.0, 1.0, -1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, -1.0, 1.0, 1.0, 0.0, 0.0,
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// n = (-1, 0, 0)
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-1.0, -1.0, 1.0, -1.0, 0.0, 0.0, -1.0, 1.0, 1.0, -1.0, 0.0, 0.0, -1.0, 1.0, -1.0, -1.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0,
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// n = (0, 1, 0)
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1.0, 1.0, -1.0, 0.0, 1.0, 0.0, -1.0, 1.0, -1.0, 0.0, 1.0, 0.0, -1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0,
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// n = (0, -1, 0)
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1.0, -1.0, 1.0, 0.0, -1.0, 0.0, -1.0, -1.0, 1.0, 0.0, -1.0, 0.0, -1.0, -1.0, -1.0, 0.0, -1.0, 0.0, 1.0, -1.0, -1.0, 0.0, -1.0, 0.0,
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]),
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indices: new Uint32Array([
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// n = (0, 0, 1)
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0, 1, 2, 2, 3, 0,
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// n = (0, 0, -1)
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4, 5, 6, 6, 7, 4,
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// n = (1, 0, 0)
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8, 9, 10, 10, 11, 8,
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// n = (-1, 0, 0)
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12, 13, 14, 14, 15, 12,
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// n = (0, 1, 0)
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16, 17, 18, 18, 19, 16,
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// n = (0, -1, 0)
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20, 21, 22, 22, 23, 20,
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]),
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vertexStride: 6, // in floats
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};
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const tests = {
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buildMeshlets: function () {
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const maxVertices = 4;
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const buffers = clusterizer.buildMeshlets(cubeWithNormals.indices, cubeWithNormals.vertices, cubeWithNormals.vertexStride, maxVertices, 512);
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const expectedVertices = [
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new Uint32Array([6, 7, 4, 5]),
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new Uint32Array([14, 15, 12, 13]),
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new Uint32Array([2, 3, 0, 1]),
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new Uint32Array([20, 21, 22, 23]),
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new Uint32Array([10, 11, 8, 9]),
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new Uint32Array([18, 19, 16, 17]),
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];
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const expectedTriangles = new Uint8Array([0, 1, 2, 2, 3, 0]);
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assert.equal(buffers.meshletCount, 6);
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for (let i = 0; i < buffers.meshletCount; ++i) {
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const m = clusterizer.extractMeshlet(buffers, i);
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assert.deepStrictEqual(m.vertices, expectedVertices[i]);
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assert.deepStrictEqual(m.triangles, expectedTriangles);
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}
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},
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computeClusterBounds: function () {
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for (let i = 0; i < 6; ++i) {
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const indexOffset = i * 6;
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const normalOffset = i * 4 * cubeWithNormals.vertexStride;
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const bounds = clusterizer.computeClusterBounds(
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cubeWithNormals.indices.subarray(indexOffset, 6 + indexOffset),
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cubeWithNormals.vertices,
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cubeWithNormals.vertexStride
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);
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assert.deepStrictEqual(
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new Int32Array([bounds.coneAxisX, bounds.coneAxisY, bounds.coneAxisZ]),
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new Int32Array(cubeWithNormals.vertices.subarray(3 + normalOffset, 6 + normalOffset))
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);
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}
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},
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computeMeshletBounds: function () {
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const maxVertices = 4;
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const buffers = clusterizer.buildMeshlets(cubeWithNormals.indices, cubeWithNormals.vertices, cubeWithNormals.vertexStride, maxVertices, 512);
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const expectedNormals = [
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new Int32Array([0, 0, -1]),
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new Int32Array([-1, 0, 0]),
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new Int32Array([0, 0, 1]),
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new Int32Array([0, -1, 0]),
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new Int32Array([1, 0, 0]),
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new Int32Array([0, 1, 0]),
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];
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const bounds = clusterizer.computeMeshletBounds(buffers, cubeWithNormals.vertices, cubeWithNormals.vertexStride);
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assert(bounds.length === 6);
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assert(bounds.length === buffers.meshletCount);
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bounds.forEach((b, i) => {
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const normal = new Int32Array([b.coneAxisX, b.coneAxisY, b.coneAxisZ]);
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assert.deepStrictEqual(normal, expectedNormals[i]);
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});
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},
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computeSphereBounds: function () {
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// positions without per-point radii (tetrahedron)
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const positions = new Float32Array([0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1]);
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const bp = clusterizer.computeSphereBounds(positions, 3);
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assert(Math.abs(bp.centerX - 0.5) < 1e-3);
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assert(Math.abs(bp.centerY - 0.5) < 1e-3);
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assert(Math.abs(bp.centerZ - 0.5) < 1e-3);
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assert(bp.radius < 0.87);
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// use 4th float of each element as radius (last point has radius=3 enveloping others)
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const radii = new Float32Array([0, 1, 2, 3]);
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const br = clusterizer.computeSphereBounds(positions, 3, radii, 1);
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assert(Math.abs(br.centerX - 1.0) < 1e-3);
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assert(Math.abs(br.centerY - 0.0) < 1e-3);
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assert(Math.abs(br.centerZ - 1.0) < 1e-3);
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assert(Math.abs(br.radius - 3.0) < 1e-3);
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},
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};
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clusterizer.ready.then(() => {
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var count = 0;
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for (var key in tests) {
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tests[key]();
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count++;
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}
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console.log(count, 'tests passed');
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});
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