Physical AI Neural Reconstruction
NVIDIAOfficialCreated by NVIDIA Physical AI
- Skill
- Meta (authoring, orchestration, planning, memory)
- Agent tooling
- Apache-2.0
- Medium risk
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 hub for NVIDIA's suite of neural reconstruction tools. It does not perform reconstruction tasks itself but identifies the correct sibling skill to handle specific stages of the pipeline, such as data conversion, training, rendering, asset harvesting, or frame harmonization. By providing a structured taxonomy and workflow ordering, it ensures users follow the correct sequence of operations—such as converting raw sensor data to NCore V4 before training or using the Asset Harvester before packaging objects into a USDZ.
What it does
- Identifies the appropriate sibling skill for specific NuRec-related tasks.
- Provides a structured workflow guide for multi-stage reconstruction pipelines.
- Maps user requests to canonical product repositories for fetching required skills.
- Maintains a registry of sibling skills including datasets, conversion tools, and rendering engines.
- Enforces best practices for secret management and gated asset access.
- Prevents duplication of logic by routing tasks to specialized upstream repositories.
When to use it
- When you need to determine which NVIDIA NuRec skill to use for a specific task.
- When starting a new neural reconstruction project and needing to understand the pipeline order.
- When you need to find or download published NVIDIA Physical AI datasets.
- When you are unsure whether to use the built-in NRE fixer or the standalone DiffusionHarmonizer.
- When you need to coordinate complex workflows involving multiple sibling skills.
Advantages
- Centralizes navigation for a complex, multi-repo ecosystem.
- Ensures consistent workflow ordering across different reconstruction tasks.
- Reduces user error by clearly defining the scope of each sibling skill.
- Provides a single point of reference for troubleshooting and setup prerequisites.
- Maintains clear boundaries between different pipelines like NuRec and SimReady.
Usage examples
- “I want to convert my LiDAR recording into NCore V4 format, what should I do?”
- The router identifies the ncore-data-conversion skill as the correct tool and provides the path to its repository.
- “How do I train a 3D reconstruction from an NCore clip?”
- The router directs you to use ncore-data-conversion first, followed by the nurec skill for training.
- “Where can I find NVIDIA's published NuRec datasets?”
- The router points you to the physical-ai-datasets skill for cataloging and downloading gated datasets.
- “I need to clean up rendered frames with ghosting issues, which skill handles this?”
- The router explains the difference between the built-in NRE fixer and the standalone harmonizer skill, guiding you to the latter for post-rendering cleanup.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/physical-ai-neural-reconstruction/downloadUnzip 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 sibling skills in their product repos
(physical-ai-datasets, ncore-data-conversion, nurec,
asset-harvester, harmonizer). Use this skill to:
Identify which sibling skill answers a NuRec question.
Name each sibling's product repo and path so it can be fetched with consent if it is not already installed.
Order multi-step NuRec workflows (data → conversion → train → render → cleanup) before opening the sibling 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, sensorsim, sensor sim, novel view synthesis,
PhysicalAI-Autonomous-Vehicles-NuRec, PhysicalAI-Robotics-NuRec,
PhysicalAI-NuRec-PPISP, Cosmos-Drive-Dreams, asset harvester,
nurec fixer, DiffusionHarmonizer, harmonizer, difix,
difix3d, carline adaptation, 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
The router itself has no runtime prerequisites beyond git for
fetching the upstream. Downstream sibling skills need Linux x86_64, an
NVIDIA GPU (Ampere+, CUDA 12.8, >= 24 GB VRAM), Docker plus the NVIDIA
Container Toolkit, an NGC API key, a Hugging Face token with the
relevant gated licenses already accepted, and Python 3.10+.
Full per-skill detail — driver floors, container names, key resolution
order, which Hugging Face assets are gated, and how to verify secrets
without echoing them — is in
references/prerequisites.md. Prefer
each sibling's scripts/validate_setup.py over hand-written checks.
What is NuRec?
NuRec (NVIDIA Omniverse Neural Reconstruction) turns camera,
LiDAR, radar, or stereo recordings — typically from a self-driving car
or a robot — into a 3D scene that can be re-rendered from any
viewpoint. A typical project runs in three stages: get the input
(convert a recording with ncore-data-conversion, or download a
ready-made dataset with physical-ai-datasets), train the
reconstruction (nurec, which emits a USDZ), then render new views
(nurec). Projects that only want to use a scene NVIDIA already
published skip the training stage.
Background on the vocabulary — NRE vs NuRec, USDZ, NCore V4, 3DGUT /
3DGRT — is in
references/what-is-nurec.md.
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-data-conversion |
| Write a new converter for an unsupported sensor setup (drone, RGB-D, ROS 2 bag, COLMAP, ScanNet++) | ncore-data-conversion |
| Train a 3D reconstruction from an NCore clip | ncore-data-conversion → nurec |
| Generate the extra inputs NRE needs (segmentation masks, depth, ego mask, DINOv2, LiDAR-seg visibility) | nurec (uses the nre-tools-ga container) |
| Render a USDZ along the original camera positions | nurec |
| Render at full resolution / highest quality | nurec (see "Quality presets") |
| Render along a shifted trajectory (e.g. car moved 3 m left) | nurec |
| Adapt an existing USDZ to an augmented target-vehicle rig (carline adaptation) | nurec (export-custom-rig-trajectory → render) → harmonizer |
| Render through a server so CARLA / Isaac Sim / AlpaSim / a custom simulator can ask for frames | nurec (serve-grpc) |
| Render the same USDZ many times back-to-back from Python with minimal per-call latency | nurec (warm serve-grpc + thin Python client / batch_render_rgb) |
| Render LiDAR sweeps (point clouds) from a USDZ | nurec (render-grpc --lidar) |
| Skip training and just render a NuRec scene NVIDIA already built | physical-ai-datasets → nurec |
| Skip training and use a pre-built indoor robotics scene | physical-ai-datasets → nurec (then Isaac Sim 5.1) |
| Extract individual 3D objects (cars, pedestrians) from a driving clip | asset-harvester |
| Add, remove, or replace cars / pedestrians in a NuRec scene | asset-harvester → nurec |
| Clean up or harmonize rendered frames (ghosting, floaters, flicker, lighting/shadows) | harmonizer, or --enable-difix inside nre for inline rendering |
| Export the scene as a PLY, mesh, depth maps, ego mask, etc. | nurec |
| Upgrade an old USDZ so newer NRE versions load it faster | nurec (upgrade-artifact) |
| Open a USDZ or PLY in a browser viewer | nurec (viewer / ply_viewer) |
| Measure rendering quality (PSNR, SSIM, LPIPS) against ground truth | nurec (eval-rendering-metrics) |
| Benchmark different reconstruction methods on the same scenes | physical-ai-datasets (PhysicalAI-NuRec-PPISP) → nurec |
| Train on multiple GPUs or on SLURM | nurec |
Common workflows
Seven end-to-end workflows are documented in
references/workflows.md, lettered A–G
by this router:
A. Make a NuRec scene from your own recording.
B. Use a NuRec scene NVIDIA has already trained.
C. Use NuRec for indoor robot simulation.
D. Add, remove, or replace 3D objects in a scene.
E. Clean up rendered frames.
F. Benchmark reconstruction quality.
G. Connect NuRec to a simulator.
Open that file when the user's task spans more than one sibling skill.
Sibling skills (upstream)
Refer to a sibling by its name — that is the portable identifier. The skill-source column is the product repo and path of that skill.
| Name | Skill source | What it does |
|---|---|---|
physical-ai-datasets | NVIDIA/nurec → skills/physical-ai-datasets/ | Catalog and download recipes for every NVIDIA Physical AI dataset on Hugging Face (driving, robotics, manipulation, NuRec scenes, benchmarks). |
ncore-data-conversion | NVIDIA/ncore → skills/ncore-data-conversion/ | Converts any sensor recording to NCore V4 (the format NRE needs). Also covers writing a new converter. |
nurec | NVIDIA/nurec → skills/nurec/ | The Neural Reconstruction Engine itself (nvcr.io/nvidia/nre/nre-ga, nvcr.io/nvidia/nre/nre-tools-ga, NRE 26.04 — image tags 26.04.01 / 26.04 / latest). Trains, performs carline adaptation, 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 | NVIDIA/asset-harvester → skills/asset-harvester/ | Open-source Apache-2.0 pipeline (SparseViewDiT + TokenGS) that extracts individual 3D objects from sparse views in a driving clip and saves them as .ply Gaussian splats, optionally emitting metadata.yaml for the NuRec handoff. |
harmonizer | NVIDIA/harmonizer → skills/harmonizer/ | 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-data-conversion vs nurec, AV-NuRec vs
Cosmos-Drive-Dreams, NuRec vs SimReady) see
references/mix-ups.md.
Locate and fetch the upstream skills
Select a sibling by its current registered name and the
repo/path in the sibling table. Full lookup, consent, and
stale-copy rules are in
references/upstream-fetch.md.
For ncore-data-conversion, nurec, and harmonizer, try the
runtime-local lookup first:
.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)
A matching installed definition can be read locally. If it is a symlink, follow it and use that definition's own companion files.
For physical-ai-datasets and asset-harvester, generic same-name
discovery cannot distinguish the old bundled copy. Use their
canonical product-repo source instead. A canonical definition
already acquired for this task can be reused.
Do not read sibling skills from a nurec-skills checkout: those
copies are retired and may still exist. Use each skill's product-repo
home. In particular, physical-ai-datasets and asset-harvester
kept their names, so a generic same-name local hit may be the old
copy. Renamed skills may resolve locally under their current names;
never fall back to the retired ncore, nre or nurec-fixer
skill folders.
If the selected current definition is unavailable, follow Ask before cloning before obtaining it from the mapped product repo. Missing a renamed local skill never falls back to an old folder or clone. If retrieval is declined, report the missing skill and stop that handoff.
Read the selected definition before following its workflow. Open its
own references on demand. Companion files (references/, scripts/,
assets/) ship inside the sibling's own skill directory. This
catalog router owns its picker and workflow letters; there is no
upstream index to fetch.
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.nurec), not by repo path. Folder layouts can change; the name is portable.Never
git clonea product repo without explicit user consent. Exhaust the local lookup order first (and the same-name exception forphysical-ai-datasetsandasset-harvester), show the exact command, and clone only into a path the user agreed to — never silently into/tmp. Do not scan broad developer workspaces such as~/Codesor reuse unrelated old clones. Never fall back to anurec-skillscheckout.Use the GA container channel:
nvcr.io/nvidia/nre/nre-gaandnvcr.io/nvidia/nre/nre-tools-ga. The un-suffixednvcr.io/nvidia/nre/nre/nre-toolsnames are the legacy channel — still valid for cached version pins, but not what a new workflow should pull.Resolve the NGC key as
${NGC_CLI_API_KEY:-${NGC_API_KEY:-}}and log in withdocker login nvcr.io --username '$oauthtoken' --password-stdin. Never echo a key.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. The owning skill for that command isnurec.For artifact cleanup, prefer the built-in
--enable-difixpath innre. Route to the standaloneharmonizeronly 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 26.04 — pull
nvcr.io/nvidia/nre/nre-ga:26.04.01or:26.04; the release namerelease_26.04is not a valid image 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. Sibling recipes live in their product repos:
https://github.com/NVIDIA/nurec,https://github.com/NVIDIA/ncore,https://github.com/NVIDIA/harmonizer, andhttps://github.com/NVIDIA/asset-harvester. A renamed skill already installed locally may be used without a network refresh; if a fetch is needed, follow Ask before cloning.Hand-curated catalogue. A newly-added upstream sibling is not discoverable here until someone edits the tables (see
references/maintenance.md).Gated content.
nvidia/PhysicalAI-*,nvidia/Harmonizer, andnvidia/Cosmos-Predict2-0.6B-Text2Imagerequire the user to accept license terms on Hugging Face first. Forasset-harvesteronly its optional DINOv3, Llama Guard and SAM 3D Body models are gated. 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 Linux x86_64 plus an NVIDIA GPU and the NVIDIA Container Toolkit. aarch64 / AMD / Intel / Apple Silicon are not supported.
No Omniverse / Isaac Sim integration steps. Handing a USDZ to Isaac Sim 5.1 (workflow C) is documented in the Isaac Sim docs, not in the NuRec skill family.
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
Routing-level symptoms — a missing upstream clone, gated-asset 403s,
NGC login failures, manifest unknown on an NRE image, stale cached
skill names — are tabulated in the troubleshooting companion file that
ships alongside this skill.
Symptoms specific to a sibling's own commands belong to that sibling's
skill.
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. Do not revoke NGC_API_KEY /
HF_TOKEN as part of teardown unless they were leaked.
Keeping this router up to date
Procedure for adding new sibling skills, renames, or upstream URL
changes lives in references/maintenance.md.
This catalog router is authoritative for the routing taxonomy and
workflow ordering. Sibling recipes live in their product repos; it
is not authoritative for asset-harvester, which is maintained in
https://github.com/NVIDIA/asset-harvester.
Files
- ATTRIBUTION.mdAdded by AutoGineer969 B
- BENCHMARK.md10.4 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md17.5 KiB
- evals/evals.json4.9 KiB
- references/maintenance.md2.5 KiB
- references/mix-ups.md2.6 KiB
- references/prerequisites.md2.5 KiB
- references/secrets-handling.md1.7 KiB
- references/teardown.md2.2 KiB
- references/troubleshooting.md3.0 KiB
- references/upstream-fetch.md3.2 KiB
- references/what-is-nurec.md1.3 KiB
- references/workflows.md5.7 KiB
- skill-card.md4.7 KiB
- skill.oms.sig6.6 KiB
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Requirements and compatibility
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
The author says: Router skill; downstream sibling skills require Linux x86_64, an NVIDIA GPU (Ampere+, CUDA 12.8, >= 24 GB VRAM), Docker >= 23.0.1, NVIDIA Container Toolkit >= 1.13.5, an NGC API key, a Hugging Face token with the relevant gated licenses accepted, Python 3.10+, and `huggingface_hub`. Optional: CARLA / Isaac Sim 5.1 / AlpaSim for simulator integration over `serve-grpc`.
Security
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Attribution and licence
- Created by
- NVIDIA Physical AI
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/physical-ai-neural-reconstruction · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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