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Physical AI Neural Reconstruction

OpenAIOfficialCreated by NVIDIA Physical AIDistributed by OpenAI

  • Skill
  • Meta (authoring, orchestration, planning, memory)
  • Data and AI
  • Apache-2.0
  • Medium risk

From the author

Router for NVIDIA NuRec/NRE: USDZ rendering, NCore conversion, 3DGS, gRPC sensor sim, PhysicalAI HF datasets. Do NOT use for SimReady or infra setup.
— the description by NVIDIA Physical AI

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Physical AI Neural Reconstruction (NuRec) Router acts as a central navigation point for NVIDIA's NuRec ecosystem. It does not perform reconstruction tasks itself but identifies the correct upstream sibling skill to handle specific stages of the pipeline, such as data conversion, training, rendering, or asset harvesting. It ensures users follow the correct sequence of operations—such as converting raw sensor data to NCore V4 before training—and provides guidance on managing dependencies like NGC API keys and gated Hugging Face datasets.

What it does

  • Routes user requests to the appropriate upstream NuRec sibling skill.
  • Provides a standardized workflow for multi-stage neural reconstruction projects.
  • Facilitates the cloning and updating of the canonical nurec-skills repository.
  • Helps users identify the correct tool for sensor simulation, rendering, and dataset management.
  • Ensures proper handling of gated Hugging Face licenses and NGC container authentication.
  • Maintains order-of-operations for complex tasks like asset harvesting and frame harmonization.

When to use it

  • When you need to identify which NuRec skill to use for a specific task.
  • Before starting any neural reconstruction project to ensure the correct workflow sequence.
  • When you need to download or locate NVIDIA-published NuRec datasets.
  • When you are unsure whether to use the built-in NRE fixer or the standalone DiffusionHarmonizer.
  • When setting up a new environment for training or rendering 3D scenes from sensor recordings.

Advantages

  • Prevents misuse of incorrect tools by clearly defining the scope of each sibling skill.
  • Centralizes the complex dependency management for NVIDIA's neural reconstruction stack.
  • Provides a clear, authoritative path for multi-step workflows like data-to-render pipelines.
  • Reduces errors by enforcing correct execution order for conversion, training, and rendering.

Usage examples

“I have a raw LiDAR recording. How do I turn it into a 3D scene?”
The router identifies the ncore skill for conversion and the nre skill for training, directing the user to follow the ncore -> nre workflow.
“Where can I find the NVIDIA driving datasets for NuRec?”
The router points the user to the physical-ai-datasets skill and provides instructions on accepting the necessary gated licenses on Hugging Face.
“How do I clean up ghosting in my rendered frames?”
The router explains the difference between the built-in --enable-difix flag in nre and the standalone nurec-fixer skill, guiding the user to the best choice.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/openai/physical-ai-neural-reconstruction/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip physical-ai-neural-reconstruction.zip -d .agents/skills

Documentation by NVIDIA Physical AI

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 publishedphysical-ai-datasets
Convert my own camera / LiDAR / radar / depth / stereo recording into NCore V4ncore
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 clipncore → 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 positionsnre
Render at full resolution / highest qualitynre (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 framesnre (serve-grpc)
Render the same USDZ many times back-to-back from Python with minimal per-call latencynre (warm serve-grpc + thin Python client / batch_render_rgb)
Render LiDAR sweeps (point clouds) from a USDZnre (render-grpc --lidar)
Skip training and just render a NuRec scene NVIDIA already builtphysical-ai-datasets → nre
Extract individual 3D objects (cars, pedestrians) from a driving clipasset-harvester
Add, remove, or replace cars / pedestrians in a NuRec sceneasset-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 fasternre (upgrade-artifact)
Open a USDZ or PLY in a browser viewernre (viewer / ply_viewer)
Measure rendering quality (PSNR, SSIM, LPIPS) against ground truthnre (eval-rendering-metrics)
Benchmark different reconstruction methods on the same scenesphysical-ai-datasets (PhysicalAI-NuRec-PPISP) → nre
Train on multiple GPUs or on SLURMnre (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)

NameUpstream folderWhat 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 / symptomLikely causeSolution
nurec-skills clone missing or emptyUpstream not fetched yetRun the clone block in Locate and fetch the upstream skills
403/401 pulling nvidia/PhysicalAI-* from HFGated license not accepted, or HF_TOKEN unset / wrong scopeAccept 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_KEYdocker 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 convertedRun the ncore skill before invoking nre
serve-grpc cold-start latency dominates a Python loopOne-shot Docker invocation per renderUse 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 missingsudo chown -R "$(id -u):$(id -g)" <output_dir>; add the -u flag next time
Frames have ghosting / floaters / flicker after renderingInline cleanup not enabledRe-render with nre --enable-difix, or post-process with nurec-fixer (DiffusionHarmonizer)
Stale skill names (ncore-data-conversion, old nvidia/Fixer) in agent outputOut-of-date cached skillUpdate references to ncore and nurec-fixer (DiffusionHarmonizer); see references/maintenance.md
Bash anti-pattern ${HF_TOKEN:+yes}${HF_TOKEN:-no} echoed token valueMisuse of bash parameter expansionRotate 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.

Files

LICENSE

Apache License
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http://www.apache.org/licenses/

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Requirements and compatibility

Written for: OpenAI Codex

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs a command-line tool

The author says: Router skill; downstream sibling skills require Docker, NVIDIA Container Toolkit, GPU, NGC API key, Hugging Face token with PhysicalAI gated licenses accepted, Python 3.10+, and `huggingface_hub`. Optional: CARLA / Isaac Sim 5.1 / AlpaSim for simulator integration over `serve-grpc`.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA Physical AI
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/plugins · plugins/nvidia/skills/physical-ai-neural-reconstruction · commit d420d13
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (written by AutoGineer)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download physical-ai-neural-reconstruction.zip