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):

bash

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

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
  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:

bash

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.

physical-ai-neural-reconstruction

NPX Install

Tags

SKILL.md Content

Physical AI Neural Reconstruction (NuRec) Router

Purpose

When to Use

Prerequisites

What is NuRec?

Pick a skill

Common workflows

Sibling skills (upstream)

Locate and fetch the upstream skills

Hard Rules

Limitations

Troubleshooting

Cross-skill teardown

Keeping this router up to date