Quick answer: Connect the graph’s point output to a Static Mesh Spawner node with a mesh selection, place the volume to enclose your sampler bounds, and click Generate or move the volume to trigger regen.
A PCG graph samples points over a landscape to scatter rocks. The PCG Inspector shows 3000 points generated, but the viewport is empty — no rocks render. The graph runs; the data flows; no geometry appears.
Points vs Geometry in PCG
PCG data flows as point sets: positions, rotations, scales, plus optional attributes. Points are abstract data, not visible geometry. To render them, you connect to a Static Mesh Spawner or Spline Mesh Spawner node, which creates ISM (Instanced Static Mesh) components on the PCG actor.
If your graph ends with the point set going to the Output node and no Spawner anywhere in the chain, the points are computed but never rendered.
The Fix: Add a Static Mesh Spawner
In the PCG graph editor:
- Right-click → Add Node → Static Mesh Spawner.
- Connect the input from your last point-processing node (e.g., Transform Points).
- Connect the Spawner’s output to the graph’s Output node.
- In the Spawner’s Details: set Mesh to a valid Static Mesh, or use Mesh Selector Weighted for variety.
Save the graph, return to the level, and force regenerate.
Verify Volume Bounds
If the sampler still produces zero points, the volume actor’s bounds are wrong:
- Select the PCG actor.
- Resize its volume to enclose the area you want to populate.
- In Details, click Generate.
For Surface Sampler specifically, the surface to sample must intersect the volume. A volume floating in space with no landscape inside produces no points.
Force a Regenerate
PCG doesn’t auto-update on every edit. Triggers include:
- Clicking Generate in the PCG actor’s Details.
- Moving or resizing the volume.
- Saving the graph (sometimes; depends on project settings).
- Calling
PCGComponent->Generate()from script.
Until a trigger fires, the cached output is what you see. Always Generate after editing the graph.
Diagnosing
Enable Inspect on a node (right-click → Inspect). The PCG Inspector panel shows point counts and attribute values flowing through that connection. If the sampler shows non-zero but the Spawner shows zero, an intermediate filter is removing all points. If the Spawner shows non-zero but no meshes appear, the Mesh field is unset or null.
Verifying
Generate the graph. Rocks should appear scattered across the landscape. Toggle the PCG actor active/inactive to confirm they’re tied to its ISM. Move the volume; on next generate, rocks should re-distribute to the new area.
Understanding the issue
This bug class falls into a pattern that's worth understanding beyond the specific case. In Unreal Engine, the underlying behavior is shaped by how the engine layers its abstractions - the public API you call, the runtime systems that respond, and the platform-specific implementations underneath. A bug at any layer can produce symptoms that look like they originate at a different layer. Triaging effectively means recognizing which layer the symptom belongs to, even when the gameplay code is what's visible.
The specific bug described above is the kind that surfaces during integration rather than unit testing. It depends on a combination of factors: the asset configuration, the runtime state, the platform's specific behavior. In isolation, each piece looks correct; in combination, the bug emerges. This is why thorough integration testing - playing the actual game in realistic conditions - catches things that automated tests miss.
Why this happens
The triage path for this kind of bug is long. The symptom appears in gameplay, but the cause is in a different system. The reporter describes the gameplay effect; the engineer has to translate that into a hypothesis about the underlying cause. Misdirection is common.
At the engine level, the behavior comes from a deliberate design decision in Unreal. The engine team chose a particular trade-off - usually performance versus convenience, or generality versus specificity - and that trade-off has consequences when you push against it. Understanding the trade-off is what turns 'this bug is mysterious' into 'this bug is the expected consequence of this design'.
Verifying the fix
For shipping games, the safest verification is a staged rollout. Apply the fix to 1% of players for 24 hours; watch the affected metric; expand if green. Skipping the staged rollout means the verification is the entire player base, which is too high a stakes for most fixes.
Reproducibility is the prerequisite for verification. If you can't reliably reproduce the bug pre-fix, you can't reliably verify it post-fix. Spend time getting a clean reproduction before you write any fix code. The fix is fast once you understand the reproduction; the reproduction is the slow part.
Variations to watch for
Related bug classes often share the same root cause. If you find yourself fixing this issue, look for cousins: similar symptoms in adjacent systems, the same data flow but a different value, or the same fix pattern in another module. The catalog of 'we've seen this before' becomes valuable institutional knowledge.
Adjacent bugs often share a root cause. After fixing the case you've found, spend an hour searching the codebase for similar patterns. What's the same call with different arguments? The same data flow with a different entity type? The same lifecycle issue in a sibling system? Each match is a candidate for the same fix, or a related fix that prevents future bugs of the same class.
In production
For shipping titles with a long support window, watch for this issue resurfacing after dependency updates. Engine upgrades, driver updates, OS releases - each one can resurface a bug class you thought you'd fixed because the underlying behavior changed slightly. Regression tests catch the obvious ones; player reports catch the rest.
When triaging a similar issue in production, prioritize gathering data over hypothesizing causes. A player report describes a symptom; what you need is a build SHA, a session timestamp, and ideally a screen recording or session replay. With those, the bug becomes tractable. Without them, you're guessing at hypothetical reproductions that may not match what the player actually hit.
Performance considerations
Performance implications matter when this bug class scales with player count or asset count. A bug that fires once per session is annoying; a bug that fires once per frame compounds. After fixing, profile the affected code path under realistic load. The fix that's correct for one entity may be too slow for ten thousand.
Diagnostic approach
Before applying any fix, gather enough context to be confident you're addressing the actual cause and not a similar-looking symptom. The cheapest diagnostic step is reproducing the bug deterministically - if you can't get the same failure twice in a row, your fix attempts will be hard to evaluate. Lock down the reproduction first.
For Unreal-specific diagnostics, the editor's profiler is the canonical starting point. Capture a representative frame with the symptom present; compare against a frame without the symptom; the diff often points directly at the cause. If the symptom is non-deterministic, capture multiple frames and look for the pattern - the cause is usually a state transition or a specific input value rather than a continuous effect.
Tooling and ecosystem
The tooling around this bug class matters as much as the fix itself. Good logging, accessible profilers, and clear error messages turn 30-minute investigations into 5-minute ones. If your project doesn't have visibility into this code path, the first fix should add the visibility - the second fix uses it.
Within Unreal, the relevant diagnostic surfaces include the standard frame debugger, memory profiler, and engine-specific debug overlays. Each one shows a different facet of what's happening. The frame debugger reveals draw call ordering and state transitions; the memory profiler shows allocation patterns; the debug overlay reveals per-system state. Bugs that resist one tool usually surrender to another - the trick is knowing which tool to reach for first.
Edge cases and pitfalls
Boundary conditions deserve specific testing attention. What happens when the input is zero, maximum, negative, or NaN? What happens at the start of a session vs hours in? What happens at the boundary between two systems handling the same data? These are where bugs hide and where regression tests are most valuable.
When writing a regression test for this fix, focus on the boundary conditions that surfaced the original bug. Tests that exercise the happy path catch obvious regressions; tests that exercise the boundary catch the subtler regressions that look like new bugs but are really the original returning. The latter are the tests that earn their keep over the long life of the project.
Team communication
When this bug class affects multiple teams (often the case for cross-system issues), early communication prevents duplicate work. The team that owns the symptom may not own the cause. A 15-minute conversation at the start of triage often saves hours of independent investigation.
If this fix touches a system several engineers work in, a short writeup in the team's engineering channel helps. Not a full design doc - a paragraph explaining what was wrong, what's fixed, and what to watch for. Future engineers encountering similar symptoms will search for the fix; making it findable is a small investment that pays back later.
“PCG points are data. Static Mesh Spawner turns them into rendered instances. Without the spawner, you’ve generated a math abstraction, not a forest.”
Inspect every connection while authoring a new graph — the moment a downstream node shows zero is the moment something went wrong.