Imported from kael-odin/codex-plugins-zh (
plugins/nvidia/skills/physical-ai-neural-reconstruction/SKILL.md). Install upstream withnpx skills add kael-odin/codex-plugins-zh --skill physical-ai-neural-reconstruction. Copyright stays with the author (Apache-2.0).
Physical AI Neural Reconstruction (NuRec) Router
Purpose
This is a thin router for NVIDIA Neural Reconstruction (NuRec)
requests. It points at the upstream nurec-index skill at
https://github.com/NVIDIA/nurec-skills and its five sibling skills
(physical-ai-datasets, ncore, nre, asset-harvester,
nurec-fixer). Use this skill to:
- Identify which upstream sibling skill answers a NuRec question.
- Locate, clone, or refresh the canonical
nurec-skillscheckout. - Order multi-step NuRec workflows (data → conversion → train → render → cleanup) before opening the upstream recipe.
The canonical recipes (training, rendering, data conversion, dataset downloads, object harvesting, frame cleanup) live in the upstream sibling skills. Never copy or reconstruct their commands here.
Do NOT use this skill for:
- SimReady packaging of CAD or source meshes → use
omniverse-cad-to-simready. - Generic USD performance tuning unrelated to NuRec → use
omniverse-usd-performance-tuning. - AKS / OSMO / NIM Operator infrastructure setup → use
physical-ai-infrastructure-setup-and-resilient-scaling.
When to Use
Read this skill first whenever a user mentions any of:
nurec, nurec router, nurec index, neural reconstruction,
neural reconstruction engine, NRE, 3DGUT, 3DGRT, USDZ,
NCore V4, sensor sim, novel view synthesis,
PhysicalAI-Autonomous-Vehicles-NuRec, PhysicalAI-NuRec-PPISP,
Cosmos-Drive-Dreams, asset harvester, nurec fixer,
DiffusionHarmonizer, harmonizer, difix, difix3d, serve-grpc,
render-grpc, warm serve-grpc, nre thin client, batch_render_rgb,
nurec teardown, "where do I start with NuRec", "which NuRec skill
should I use for X?".
Decide which upstream sibling skill answers the question, fetch it (see Locate and fetch the upstream skills), then follow that skill's body.
Prerequisites
Router skill itself has no runtime prerequisites beyond git for
fetching the upstream. Downstream sibling skills require:
- Docker + NVIDIA Container Toolkit + GPU — for
nre,nre-tools, andnurec-fixercontainers (nvcr.io/nvidia/nre/nre,nvcr.io/nvidia/nre/nre-tools,nvcr.io/nvidia/cosmos/cosmos-predict2-container:1.2). - NGC API key (
NGC_API_KEY) — for pulling NGC containers. - Hugging Face token (
HF_TOKEN) with thenvidia/PhysicalAI-*,nvidia/DiffusionHarmonizer, andnvidia/asset-harvestergated licenses accepted in advance on Hugging Face. - Python 3.10+ with
huggingface_hubinstalled. - (Optional) CARLA, Isaac Sim 5.1, or AlpaSim for simulator
integration over
serve-grpc.
Verify secrets safely (do not echo values):
hf auth whoami
[ -n "${HF_TOKEN:-}" ] && echo "HF_TOKEN length=${#HF_TOKEN}" || echo "HF_TOKEN unset"
[ -n "${NGC_API_KEY:-}" ] && echo "NGC_API_KEY length=${#NGC_API_KEY}" || echo "NGC_API_KEY unset"See references/secrets-handling.md
for the bash anti-patterns to avoid.
What is NuRec?
NuRec (NVIDIA Omniverse Neural Reconstruction) takes camera, LiDAR, radar, or stereo recordings — typically from a self-driving car or a robot — and turns them into a 3D scene you can re-render from any viewpoint. Names that come up a lot:
- NRE — "Neural Reconstruction Engine". NuRec is the product; NRE
is the engine that trains and renders. Both route to the upstream
nreskill. - USDZ — the file format of a trained scene. A zip archive that Omniverse, Isaac Sim, and CARLA can open.
- NCore V4 — the input format NRE consumes. Raw recordings must be converted to NCore V4 before training.
- 3DGUT / 3DGRT — the two 3D Gaussian Splatting flavours used internally by NRE. The default Hydra recipe picks one; most users never set it manually.
A typical NuRec project has three stages:
- Get the input — convert your own recording to NCore V4
(
ncore), or download a pre-converted dataset (physical-ai-datasets). - Train the reconstruction — feed NCore V4 to NRE; out comes a
USDZ (
nre). - Render new views — render images, videos, or LiDAR sweeps from
the USDZ (
nre).
Projects that just want to use an existing NVIDIA-published scene skip step 2.
Pick a skill
Match the user's goal in the left column and open the named upstream skill on the right. Arrows mean "do these in order".
| I want to… | Upstream skill |
|---|---|
| Find or download a NuRec dataset NVIDIA has published | physical-ai-datasets |
| Convert my own camera / LiDAR / radar / depth / stereo recording into NCore V4 | ncore |
| Write a new converter for an unsupported sensor setup (drone, RGB-D, ROS 2 bag, COLMAP, ScanNet++) | ncore |
| Train a 3D reconstruction from an NCore clip | ncore → nre |
| Generate the extra inputs NRE needs (segmentation masks, depth, ego mask) | nre (uses the nre-tools container) |
| Render a USDZ along the original camera positions | nre |
| Render at full resolution / highest quality | nre (see "Quality presets") |
| Render along a shifted trajectory (e.g. car moved 3 m left) | nre |
| Render through a server so CARLA / Isaac Sim / AlpaSim / a custom simulator can ask for frames | nre (serve-grpc) |
| Render the same USDZ many times back-to-back from Python with minimal per-call latency | nre (warm serve-grpc + thin Python client / batch_render_rgb) |
| Render LiDAR sweeps (point clouds) from a USDZ | nre (render-grpc --lidar) |
| Skip training and just render a NuRec scene NVIDIA already built | physical-ai-datasets → nre |
| Extract individual 3D objects (cars, pedestrians) from a driving clip | asset-harvester |
| Add, remove, or replace cars / pedestrians in a NuRec scene | asset-harvester → nre |
| Clean up or harmonize rendered frames (ghosting, floaters, flicker, lighting/shadows) | nurec-fixer, or --enable-difix inside nre for inline rendering |
| Export the scene as a PLY, mesh, depth maps, ego mask, etc. | nre |
| Upgrade an old USDZ so newer NRE versions load it faster | nre (upgrade-artifact) |
| Open a USDZ or PLY in a browser viewer | nre (viewer / ply_viewer) |
| Measure rendering quality (PSNR, SSIM, LPIPS) against ground truth | nre (eval-rendering-metrics) |
| Benchmark different reconstruction methods on the same scenes | physical-ai-datasets (PhysicalAI-NuRec-PPISP) → nre |
| Train on multiple GPUs or on SLURM | nre (Workflow D) |
Common workflows
Six end-to-end workflows are documented in
references/workflows.md:
- A. Make a NuRec scene from your own recording.
- B. Use a NuRec scene NVIDIA has already trained.
- C. Add, remove, or replace 3D objects in a scene.
- D. Clean up rendered frames.
- E. Benchmark reconstruction quality.
- F. Connect NuRec to a simulator.
Open that file when the user's task spans more than one sibling skill.
Sibling skills (upstream)
| Name | Upstream folder | What it does |
|---|---|---|
physical-ai-datasets |
.agents/skills/physical-ai-datasets/ |
Catalog and download recipes for every NVIDIA Physical AI dataset on Hugging Face (driving, robotics, manipulation, NuRec scenes, benchmarks). |
ncore |
.agents/skills/ncore/ |
Converts any sensor recording to NCore V4 (the format NRE needs). Also covers writing a new converter. |
nre |
.agents/skills/nre/ |
The Neural Reconstruction Engine itself. Trains, renders (locally, via warm serve-grpc + thin Python client / batch_render_rgb, or to an external simulator), exports meshes / point clouds / depth, edits actors, evaluates quality. |
asset-harvester |
.agents/skills/asset-harvester/ |
Open-source Apache-2.0 pipeline that extracts individual 3D objects from sparse views in a driving clip and saves them as .ply Gaussian splats with metadata. |
nurec-fixer |
.agents/skills/nurec-fixer/ |
Standalone NVIDIA DiffusionHarmonizer workflow — public successor to the older Fixer / Difix3D+ recipes — that cleans rendered frames, harmonizes inserted actors, evaluates PSNR/LPIPS, and optionally fine-tunes the model. |
For naming overlaps (NRE vs Fixer, ncore vs nre, AV-NuRec vs
Cosmos-Drive-Dreams, NuRec vs SimReady) see
references/mix-ups.md.
Locate and fetch the upstream skills
Quick recipe (full version in
references/upstream-fetch.md):
UPSTREAM_ROOT="${NUREC_SKILLS_UPSTREAM_ROOT:-${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}}"
mkdir -p "$UPSTREAM_ROOT"
if [ -d "$UPSTREAM_ROOT/nurec-skills/.git" ]; then
git -C "$UPSTREAM_ROOT/nurec-skills" fetch --tags
git -C "$UPSTREAM_ROOT/nurec-skills" checkout main
git -C "$UPSTREAM_ROOT/nurec-skills" pull --ff-only
else
git clone --depth 1 https://github.com/NVIDIA/nurec-skills.git \
"$UPSTREAM_ROOT/nurec-skills"
fi
test -f "$UPSTREAM_ROOT/nurec-skills/.agents/skills/SKILL.md"Then read the upstream skill before running any mutating command:
cat "$UPSTREAM_ROOT/nurec-skills/.agents/skills/SKILL.md" # router
cat "$UPSTREAM_ROOT/nurec-skills/.agents/skills/<folder>/SKILL.md" # siblingLocal lookup order (try in order before the upstream clone):
.agents/skills/<name>/SKILL.md(Cursor, Codex, NemoClaw).claude/skills/<name>/SKILL.md(Claude Code).cursor/skills/<name>/SKILL.md(project-scoped)~/.cursor/skills/<name>/SKILL.md(personal skills)
Hard Rules
- Router only — do not duplicate upstream NuRec recipes here. Read the upstream sibling skill body before running any mutating command.
- Refer to sibling skills by their
name:(e.g.nre), not by repo path. Folder layouts can change; the name is portable. - Clone or refresh
https://github.com/NVIDIA/nurec-skillsunder the shared upstream root (${NUREC_SKILLS_UPSTREAM_ROOT:-${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}}/nurec-skills). Do not scan broad developer workspaces such as~/Codesor reuse unrelated old clones. physical-ai-datasetscovers gated Hugging Face datasets. Do not bypass dataset license terms; the user must accept thePhysicalAI-*gated licenses on Hugging Face and provide a token before downloading.- Asset Harvester runs before packaging into a USDZ. Do not call
nre'sexport-external-assetson hand-rolled.plyfiles unless the user explicitly asks to skip Asset Harvester. - For artifact cleanup, prefer the built-in
--enable-difixpath innre. Route to the standalonenurec-fixeronly when the user needs the public code/model card, paired evaluation, fine-tuning, or fixes on previously rendered frames. - Do not invent NRE / NCore / DiffusionHarmonizer commands from
memory. Re-read the upstream sibling skill — versions move fast
(NRE
release_26.04is the current pinned tag). - This router does not deploy infrastructure. Route AKS / OSMO /
NIM Operator setup to
physical-ai-infrastructure-setup-and-resilient-scaling.
Limitations
- Router only. This skill never executes mutating NuRec commands. All training, rendering, conversion, and harmonization happens in upstream sibling skills.
- Upstream-pinned. Recipes live in
https://github.com/NVIDIA/nurec-skills, which evolves outside this repo. Stale clones can drift; alwaysgit pullthe upstream before relying on a sibling skill. - Gated content.
nvidia/PhysicalAI-*,nvidia/DiffusionHarmonizer, andnvidia/asset-harvesterrequire the user to accept license terms on Hugging Face first. The router cannot bypass this. - Heavy footprint. A complete NuRec workflow can leave 150 GB+
on disk. See
references/teardown.md. - NVIDIA-only stack. Requires an NVIDIA GPU plus the NVIDIA Container Toolkit. AMD / Intel / Apple Silicon are not supported.
- Not a SimReady pipeline. NuRec produces a renderable USDZ from
a recording; SimReady packaging of CAD or source meshes is a
different pipeline (see
omniverse-cad-to-simready).
Troubleshooting
| Error / symptom | Likely cause | Solution |
|---|---|---|
nurec-skills clone missing or empty |
Upstream not fetched yet | Run the clone block in Locate and fetch the upstream skills |
403/401 pulling nvidia/PhysicalAI-* from HF |
Gated license not accepted, or HF_TOKEN unset / wrong scope |
Accept the gated license on Hugging Face, then hf auth login with a token that has read access |
denied: requested access to the resource is denied from nvcr.io/nvidia/nre/* |
Missing or expired NGC_API_KEY |
docker login nvcr.io with $oauthtoken / NGC_API_KEY; rotate the key at org.ngc.nvidia.com/setup/api-key if needed |
| NRE refuses to load a clip ("not valid NCore V4") | Recording was not converted | Run the ncore skill before invoking nre |
serve-grpc cold-start latency dominates a Python loop |
One-shot Docker invocation per render | Use the nre warm serve-grpc + thin Python client (batch_render_rgb) recipe |
Output files are owned by root after a docker run |
-u $(id -u):$(id -g) was missing |
sudo chown -R "$(id -u):$(id -g)" <output_dir>; add the -u flag next time |
| Frames have ghosting / floaters / flicker after rendering | Inline cleanup not enabled | Re-render with nre --enable-difix, or post-process with nurec-fixer (DiffusionHarmonizer) |
Stale skill names (ncore-data-conversion, old nvidia/Fixer) in agent output |
Out-of-date cached skill | Update references to ncore and nurec-fixer (DiffusionHarmonizer); see references/maintenance.md |
Bash anti-pattern ${HF_TOKEN:+yes}${HF_TOKEN:-no} echoed token value |
Misuse of bash parameter expansion | Rotate the token; use hf auth whoami or length-only checks (see references/secrets-handling.md) |
Cross-skill teardown
A complete NuRec workflow can leave 150 GB+ on disk between
container images, model weights, code clones, conda envs, and output
directories. Each sibling skill has its own dedicated Teardown
section — read them in the order documented in
references/teardown.md when the user no
longer needs the workflow.
Keeping this router up to date
Procedure for adding new sibling skills, renames, or upstream URL
changes lives in references/maintenance.md.
Treat the upstream nurec-index at
https://github.com/NVIDIA/nurec-skills/blob/main/.agents/skills/SKILL.md
as authoritative; this skill mirrors only the picker tables, the
workflow ordering, and the upstream fetch recipe.
