physical-ai-neural-reconstruction

The full skill.

Original instructions from the publisher’s SKILL.md

# 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 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`, `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](#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`](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`, or download a ready-made dataset
with `physical-ai-datasets`), train the reconstruction (`nre`, which
emits a USDZ), then render new views (`nre`). 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`](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` |
| 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, DINOv2, LiDAR-seg visibility) | `nre` (uses the `nre-tools-ga` 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` |
| Adapt an existing USDZ to an augmented target-vehicle rig (carline adaptation) | `nre` (`export-custom-rig-trajectory` → `render`) → `nurec-fixer` |
| 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` |
| Skip training and use a pre-built indoor robotics scene | `physical-ai-datasets` → `nre` (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` → `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` |

## Common workflows

Seven end-to-end workflows are documented in
[`references/workflows.md`](references/workflows.md), lettered to
match the upstream `nurec-index` workflow IDs:

- **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 folder column is where it lives in a local `nurec-skills` checkout,
except where a repo is named — `asset-harvester` ships from its own
product repo.

| Name | Upstream folder | What it does |
|------|-----------------|--------------|
| `physical-ai-datasets` | `skills/physical-ai-datasets/` | Catalog and download recipes for every NVIDIA Physical AI dataset on Hugging Face (driving, robotics, manipulation, NuRec scenes, benchmarks). |
| `ncore` | `skills/ncore/` | Converts any sensor recording to NCore V4 (the format NRE needs), upstream release `2026.04`. Also covers writing a new converter. |
| `nre` | `skills/nre/` | 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`](https://github.com/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. |
| `nurec-fixer` | `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`](references/mix-ups.md).

## Locate and fetch the upstream skills

Try the local disk first, in this order — a sibling skill already
installed in the runtime is preferable to a network fetch. This applies
to the `nurec-skills`-hosted siblings; `asset-harvester` is fetched from
its own repo (see `references/upstream-fetch.md`):

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)
5. An existing `nurec-skills` clone under the shared upstream root.

**This order covers the `nurec-skills`-hosted siblings only.**
`asset-harvester` is not among them — see
[`references/upstream-fetch.md`](references/upstream-fetch.md).

**Only if none of those exist**, ask the user for explicit consent
before cloning. A `git clone` is a network fetch of an external
repository plus a write to the local filesystem; it can violate
org network policy and carries supply-chain risk. Show the user
what you intend to run and wait for a yes.

Quick recipe (full version, including the pinned-commit layout, in
[`references/upstream-fetch.md`](references/upstream-fetch.md)):

```bash
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
  # Only after the user has agreed. Prefer --branch <tag-or-sha> over HEAD.
  git clone --depth 1 https://github.com/NVIDIA/nurec-skills.git \
    "$UPSTREAM_ROOT/nurec-skills"
fi
test -f "$UPSTREAM_ROOT/nurec-skills/skills/nurec-index/SKILL.md"
```

The upstream tree is rooted at `skills/<name>/SKILL.md`;
`.agents/skills` is a symlink onto `skills/`, so either path
resolves. Read the upstream skill before running any mutating
command:

```bash
cat "$UPSTREAM_ROOT/nurec-skills/skills/nurec-index/SKILL.md"  # upstream router
cat "$UPSTREAM_ROOT/nurec-skills/skills/<folder>/SKILL.md"     # sibling
```

Companion files (`references/`, `scripts/`, `assets/`) ship inside
**the sibling's own skill directory**, alongside its skill definition
— not next to this router.

## 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.
- **Never `git clone` the upstream without explicit user consent.**
  For the `nurec-skills` siblings, exhaust the local lookup order
  first, 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 `~/Codes` or
  reuse unrelated old clones.
- Use the GA container channel: `nvcr.io/nvidia/nre/nre-ga` and
  `nvcr.io/nvidia/nre/nre-tools-ga`. The un-suffixed
  `nvcr.io/nvidia/nre/nre` / `nre-tools` names 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 with `docker login nvcr.io --username '$oauthtoken'
  --password-stdin`. Never echo a key.
- `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 26.04 — pull `nvcr.io/nvidia/nre/nre-ga:26.04.01` or `:26.04`; the release name
  `release_26.04` is not a valid image tag — and NCore `2026.04` are the current pins).
- 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.** Most recipes live in
  `https://github.com/NVIDIA/nurec-skills`; `asset-harvester` lives in
  `https://github.com/NVIDIA/asset-harvester`, which evolves outside
  this repo. Stale clones can drift; always refresh the upstream
  before relying on a sibling skill.
- **Hand-curated catalogue.** A newly-added upstream sibling is not
  discoverable here until someone edits the tables (see
  [`references/maintenance.md`](references/maintenance.md)).
- **Gated content.** `nvidia/PhysicalAI-*`, `nvidia/Harmonizer`, and
  `nvidia/Cosmos-Predict2-0.6B-Text2Image` require the user to accept
  license terms on Hugging Face first. For `asset-harvester` only 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`](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 `403`s,
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`](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`](references/maintenance.md).
Treat the upstream `nurec-index` at
<https://github.com/NVIDIA/nurec-skills/blob/main/skills/nurec-index/SKILL.md>
as authoritative **for the routing taxonomy and workflow ordering**;
this skill mirrors only the picker tables, the workflow ordering, and
the upstream fetch recipe. It is not authoritative for
`asset-harvester`, which is maintained in
<https://github.com/NVIDIA/asset-harvester>.