three.js meshlets: what the GPU-driven pipeline does and does not copy from Nanite

mdjsonmcpcopy↗ai

2026-10-31 · 15 min · webgpu · three-js · gpu-driven-rendering · computer-graphics

Why read this

Solid· top 85%

Reads the merged pipeline source, counts the demo's 819M triangles by running its geometry, and re-implements clustering and cone/frustum culling with numbers.

• Original analysis
• Runs on a consumer GPU
• Widely used

·GPUs, kernels & systems·MIT·Practitioner tool

How this was scored

Is it new?

1 of 3: An incremental tweak

Can I trust it?

2 of 3: Measures key facts from files, code or configs

Can I run it?

2 of 3: Open code or weights with real limits

Will I understand it?

2 of 3: Mechanism from first principles with figures

Can I act on it?

1 of 3: General advice

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1 of 3: Relevant for months

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2 of 3: A widely used model, tool or lab release

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2 of 3: A teardown or measurement few others did

Score 57 of 100, ranked 462 of 666 rated articles. Each question is answered 0–3 by hand, and a 3 is rare. How articles are scored

A post went around saying three.js had merged "a Nanite-like" meshlet pipeline, with a demo "rendering 1B triangles at 150 FPS". The PR is mrdoob/three.js#35191, by sunag, titled WebGPURenderer: Introduce StandardDrawingPipeline, MeshletDrawingPipeline and VisibilityBufferDrawingPipeline. It closed on 29 October. The demo the post names, webgpu_gpudriven, has since been renamed to webgpu_meshlets (PR 35282).

I did not see the post's frame rate, and I have no GPU in this sandbox, so the 150 FPS is reported and stays that way. What I can check is the code. This article does four things. It reads the pipeline. It counts what the demo actually draws. It re-implements the cluster builder and the culling tests on the CPU and measures them. And it lines the result up against Nanite, because "Nanite-like" is the claim worth being exact about.

Labels used below: measured means I ran it here. Read means I read it in the source at the commit named. Reported means someone else said it. Reasoned means I derived it from the above and did not run it.

What I could and could not run

The repo is cloned shallow at cb0a902b668f (30 October), a day or so after the merge, so it includes follow-up changes. I read these files: examples/jsm/pipelines/MeshletDrawingPipeline.js (5,571 lines), VisibilityBufferDrawingPipeline.js (954), utils/MeshletGeometryUtils.js (417), and examples/webgpu_meshlets.html.

I ran the demo's own TeapotGeometry(1, 10) under Node 22 to count triangles. That is the only repository code I ran. I also tried to run the repo's cluster builder (the meshoptimizer WASM it calls) on CPU for a head-to-head. The sandbox's permission layer refused the command, and I did not work around it. So every clustering number below is from my own numpy implementation, not from the pipeline's builder. Where that matters I say so.

I never touched a GPU. Anything about frame time is reported or absent.

Meshlets in one paragraph

A mesh has tens of thousands of triangles. Testing each one against the camera is too fine a grain, and testing the whole mesh is too coarse. A meshlet is the middle: a small cluster of neighbouring triangles, built once on the CPU, that carries its own bounding sphere and a "normal cone" (the range of directions its triangles face). The GPU can then cull a few hundred meshlets per object instead of thousands of triangles, and draw only the survivors. Hardware mesh shaders settled on 64 vertices and 124 triangles. three.js has no mesh shaders in WebGPU, so it picks something different.

How three.js builds them

createMeshlets() in MeshletGeometryUtils.js does this for each geometry (read):

1. Weld identical vertices by hashing position, normal, uv and any extra attributes. A comment says why: non-indexed input would otherwise give clusters of about 21 triangles and no LODs.
2. Simplify the index buffer through a chain of up to six levels, with target triangle ratios 1, 0.55, 0.25, 0.1, 0.04, 0.015 and growing error budgets. If a level removes less than 15 percent, it retries with simplifySloppy and a four-times larger error.
3. Per level, call MeshoptClusterizer.buildMeshlets(indices, positions, 3, 192, 64, 0.25) and computeMeshletBounds.
4. Pad each cluster with degenerate triangles to exactly 64, and pack indices as Uint32 into a pool. Bounds go in a 32-byte record: sphere xyz + radius, cone axis xyz + cutoff.

Two details differ from the hardware convention. The vertex cap is 192, not 64 (MAX_MESHLET_VERTICES = MESHLET_SIZE * 3), because the draw pulls vertices by global index, so a cluster is never limited by a local vertex buffer. And every cluster is padded to 64 triangles, so a cluster with 30 real triangles still costs 64 slots. The file's own comment says faceted geometry would otherwise end clusters at about 32 triangles.

The frame, stage by stage

MeshletCulling builds a chain of compute passes (read). Per frame:

• Object pass, one thread per object. Transforms the geometry's bounding sphere, tests six frustum planes, applies a per-object LOD range, computes a screen radius and drops objects under a minimum, and picks a LOD level by projected error: the last level whose error * scale * pixelFactor is under lodThreshold. Then it reserves its clusters with one atomicAdd per workgroup, not per thread.
• Dispatch args. One thread turns the cluster counter into a workgroup count, ceil(n / 64), split into rows of 65,535 groups.
• Cluster pass, one thread per cluster. Cone test, then six planes, then an optional hierarchical-Z test. Survivors are appended to a work queue with an atomicAdd.
• Draw args. One thread per queue writes [192, visible, 0, 0]: vertex count MESHLET_SIZE * 3, instance count the number of queued clusters.
• Draw. geometry.setIndirect(argsAttribute, offset), a plain indirect instanced draw. As I read it, the vertex shader looks up the work queue by instance index and the pool by vertexIndex.

That last point matters for the word "GPU-driven". It is not multiDrawIndirect, which WebGPU does not have. It is one indirect draw per batch, where a batch is the objects sharing a geometry and a material, with a near and a far queue. The CPU never learns the visible count unless it reads a counter back; the code reads counters every 30 projections to resize its budgets.

The widget runs the arithmetic of that chain on the demo's constants. Its level sizes are the nominal ratios, so they are reasoned, not measured on a GPU.

The shader line that does the cone test

This is the cluster-level test, copied from createClusterCull:

const coneAxis = normalize( matrixWorld.mul( vec4( cone.xyz, 0.0 ) ).xyz );
const toCluster = chunkCenter.sub( cameraPosition );
 
If( dot( toCluster, coneAxis ).greaterThanEqual( cone.w.mul( length( toCluster ) ).add( chunkRadius ) ), () => {
	chunkVisible.assign( false );
} );

In words: a cluster is culled when the vector from the camera to its centre, projected on the cone axis, exceeds cutoff * distance + radius. The radius term is the slack that makes it safe from the cluster's centre rather than from every triangle's own position. It also explains why the test loosens as the camera gets close, which the toy below shows. It is gated by a flag, skipped in shadow passes, skipped for deformed positions, and requires uniform positive scale because the cone is transformed by the world matrix.

What I measured: building clusters

I wrote a greedy builder in numpy (exp/mesh.py, not committed) and ran it on two meshes I generated: an icosphere at subdivision 5 (10,242 vertices, 20,480 triangles) and a (2,3) torus knot with a 0.3 tube (14,400 vertices, 28,800 triangles). The rule: seed next to the last cluster's frontier, then repeatedly add the neighbouring triangle with the fewest new vertices, ties broken by centroid distance. I ran it at the pipeline's 64-triangle cap, and at the hardware convention of 64 vertices and 124 triangles for comparison.

Measured, CPU, my builder. The vertex cap never binds at 64 triangles (a cluster of 64 triangles touches about 30 vertices), so the 192 cap in three.js is moot for smooth meshes; it matters for faceted ones.

The unflattering number is the mean. A good clusterer should land close to the cap on a smooth closed mesh. Mine does not: on the icosphere at the 124-triangle cap the median is 96 but the 10th percentile is 1 triangle, and the first half of the clusters average 88 triangles while the second half average 44. That is the signature of greedy growth: the first clusters eat whole regions, and the late ones are the scraps trapped between them. 36 percent of clusters end under half full. For a 64-triangle fixed layout, the pad waste is 35 percent of the slots, spent on degenerate triangles that the GPU still has to fetch and discard.

I cannot say how much of this three.js suffers. meshoptimizer's builder is a better algorithm than mine (it scores candidates with cone and compactness terms and chooses seeds more carefully), and I could not run it. The fair reading: the padding cost is real by construction, the size of it depends on the builder, and my 35 percent is an upper-ish bound from a naive one.

A second caveat about reuse. "Vertex loads per triangle" of 0.73 says a cluster shares vertices well internally: unclustered, each triangle would load three. But three.js pulls vertices by global index per triangle corner, with no per-cluster vertex cache. Whether the 0.73 turns into saved memory traffic depends on the GPU's own post-transform cache. That is reasoned, and I did not measure it.

What I measured: culling

For each mesh I placed a camera in several positions, built each cluster's sphere and cone, and applied the same two tests the shader does. I also computed the truth directly per triangle: a triangle is invisible if it faces away from the camera, or all three of its vertices fall outside one frustum plane. Occlusion is not in the truth, since neither test sees it.

Icosphere, 64-triangle cap, 50 degree vertical FOV, 16:9 (measured):

Torus knot, same settings:

Three things to take from these.

1. No false culls. Across all nine scenarios in each of three builder configs, the number of truly visible triangles inside a culled cluster was zero. The test is conservative, as written.
2. The test leaves a lot behind, and the amount depends on shape. A sphere is the best case, since every cluster's cone is narrow and consistent. The cone catches 91 to 95 percent of the clusters that are truly back-facing. The torus knot is curved in both directions, so its clusters carry wide cones, and the cone discards about half of the invisible triangles. It is not unusable cones: about 97 percent of the clusters in both meshes had a cutoff below 1. It is cones too wide to reject at most angles.
3. Frustum culling is the big win for the camera that looks away, and it is free at this level: one sphere against six planes. Looking 90 degrees off culls the whole object, which the object-level pass does before any cluster thread runs.

What these numbers are not: they are not a measurement of the pipeline. They use my clusters, not meshoptimizer's, on meshes with no occluders. The pipeline adds the optional two-phase hierarchical-Z occlusion test, and the code skips it automatically when it culls under 30 percent of visible clusters (OCCLUSION_MIN_CULLED = 0.3), which is a decent hint of how much that test costs when it does not pay.

What the demo draws

webgpu_meshlets.html builds rows = cols = 360, so instanceCount = 129,600 teapots, each new TeapotGeometry(1, 10). The page computes totalTriangles = instanceCount * geometry.index.count / 3 and prints it. I ran that geometry:

TeapotGeometry(1, 10): 6320 triangles, 3872 vertices
129,600 x 6,320 = 819,072,000 triangles

Measured (the geometry), arithmetic (the product). So the demo at this commit is 0.82 billion triangles at full detail, not 1 billion. The post may quote an earlier revision, or round up; I cannot tell from here, and the example was changed between the merge and my clone (it was renamed). That is a ceiling on what the scene contains, not on what is drawn: LOD selection picks coarser levels per teapot by screen error, and the object and cluster culls drop the rest. By the nominal ratios the coarsest level of a teapot is about 95 triangles, so a scene entirely at that level is about 12 million.

The comment in the page about the visibility buffer addressing "up to 131072 objects per render list" matters here: 129,600 is just under 2^17 = 131,072, the cap from INSTANCE_INDEX_BITS = 17. The scene was sized to fit.

I do not know what GPU the 150 FPS came from, what resolution, what mode (Meshlet Debug colours by cluster), which LOD threshold, or whether occlusion culling was on (the demo turns it off). A figure of that kind is meaningful for one chip at one setting. Treat it as reported.

Nanite, item by item

"Nanite-like" is fair in spirit and wrong in several details. Here is what is in the source against the Nanite design as publicly described.

The correction to the common summary is the software rasterizer: it exists, in the visibility-buffer variant, and it is the part most closely modelled on Nanite. The software path packs depth into the high bits of a 32-bit word so atomicMax resolves both the depth test and the payload in one operation. The MeshletDrawingPipeline I walked through above uses only hardware rasterization; the visibility-buffer pipeline extends it.

What is missing is what makes Nanite virtualized. With a per-object LOD chain, the choice is per object: the silhouette of a mountain is as detailed as its foot. A DAG with per-cluster error would let the near part use fine clusters and the far part use coarse ones within one mesh. And nothing here streams: the whole chain is resident, so the scene is bounded by GPU memory. For the teapot chain, nominally 195 clusters at 800 bytes each (768 of indices plus 32 of bounds), that is about 150 KB per mesh, which is tiny. For a scanned statue with millions of triangles, the chain is the budget.

What it costs, and where it breaks

Reading the guards tells you where the author hit walls:

• Materials with vertexNode, fragmentNode, depthNode, alphaTestNode and similar are not supported by the meshlet draw. The visibility buffer's software path additionally skips alpha-tested materials and MRT render calls, which go through the hardware path.
• Cone culling needs uniform positive scale, and is off in shadow passes and for materials that move vertices.
• The cluster budget is fixed (CLUSTERS_PER_OBJECT = 8 with a minimum of 4M); clusters over the budget are not drawn. The code grows budgets after reading counters back, so I expect overflow to show up as missing clusters for a few frames, not a crash (reasoned; I did not provoke it).
• LOD pops, as I read it: per-object selection by projected error has no blending in the code I saw.

None of these is a flaw; they are the shape of the first version of a big feature. They are why I would not read "Nanite-like" as "drop-in Nanite".

What to take from it

If you ship three.js, the useful questions are narrower than the headline:

1. Is your scene many instances of few geometries? Batches are keyed by geometry and material, and that is where the indirect draw pays.
2. Does your material fit the allowed node set? Check UNSUPPORTED_NODES.
3. Are your meshes smooth enough for cones to cull? A sphere gave me 48 to 73 percent cluster culls by cone alone; a knot gave 30. Faceted CAD will do differently again, and you can measure it on your own meshes with the twenty lines of numpy that matter: bounds, cone, one dot product.
4. Reproduce the headline on your own hardware. The example is public, the Rasterizer dropdown switches between Basic, Meshlet and Visibility Buffer, and that comparison on your GPU is worth more than any quoted FPS.

What I did not do: run any WebGPU code, measure a frame time, run meshoptimizer's builder, read the PR discussion (my GitHub access to the repository was limited to search results), or check the post's claims beyond the code. The 150 FPS and the 1B are the post's; the 819,072,000 and the architecture are what I can stand behind.