Physical AI Neural Reconstruction

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:

• 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" # sibling

Local 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-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.