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-skills checkout.
• 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, and nurec-fixer containers (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 the nvidia/PhysicalAI-*, nvidia/DiffusionHarmonizer, and nvidia/asset-harvester gated licenses accepted in advance on Hugging Face.
• Python 3.10+ with huggingface_hub installed.
• (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 nre skill.
• 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:

1. Get the input — convert your own recording to NCore V4 (ncore), or download a pre-converted dataset (physical-ai-datasets).
2. Train the reconstruction — feed NCore V4 to NRE; out comes a USDZ (nre).
3. 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" # sibling

Local lookup order (try in order before the upstream clone):

1. .agents/skills/<name>/SKILL.md (Cursor, Codex, NemoClaw)
2. .claude/skills/<name>/SKILL.md (Claude Code)
3. .cursor/skills/<name>/SKILL.md (project-scoped)
4. ~/.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-skills under 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 ~/Codes or reuse unrelated old clones.
• physical-ai-datasets covers gated Hugging Face datasets. Do not bypass dataset license terms; the user must accept the PhysicalAI-* gated licenses on Hugging Face and provide a token before downloading.
• Asset Harvester runs before packaging into a USDZ. Do not call nre's export-external-assets on hand-rolled .ply files unless the user explicitly asks to skip Asset Harvester.
• For artifact cleanup, prefer the built-in --enable-difix path in nre. Route to the standalone nurec-fixer only 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_26.04 is 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; always git pull the upstream before relying on a sibling skill.
• Gated content. nvidia/PhysicalAI-*, nvidia/DiffusionHarmonizer, and nvidia/asset-harvester require 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.