nurec-index

name	nurec-index
description	Router for NVIDIA NuRec / NRE / 3DGUT / USDZ / NCore V4 / asset harvest / frame cleanup tasks — picks the right sibling (nre, ncore, asset-harvester, nurec-fixer, physical-ai-datasets). Use when the sub-skill is unclear or a multi-stage pipeline is needed; do NOT use for non-NuRec tasks or to run any pipeline itself.
version	0.2.4
tools	["Read"]
license	CC-BY-4.0 AND Apache-2.0
metadata	{"author":"NVIDIA NRS <[e-mail]>","tags":["nurec","index","router","table-of-contents"],"canonical_repo":"https://github.com/NVIDIA/nurec-skills","canonical_skills_dir":".agents/skills/","agentskills_io_compatible":true,"trigger_keywords":["nurec","nurec index","nurec router","neural reconstruction engine","NRE","3DGUT","3DGRT","USDZ","sensorsim","novel view synthesis","PhysicalAI-Autonomous-Vehicles-NuRec","NuRec pipeline","NuRec workflow","warm serve-grpc","nre thin client","batch_render_rgb","nurec teardown"]}

NuRec Skills Index

A routing skill. It decides which sibling skill to read next for a NuRec / Neural Reconstruction task. Five siblings cover the full pipeline:

• physical-ai-datasets — find an existing NVIDIA dataset.
• ncore — convert raw sensor data into NCore V4.
• nre — train a 3DGUT reconstruction and render novel views from NCore V4 (or a pre-trained USDZ).
• asset-harvester — extract per-object 3D Gaussian Splat assets from sparse AV-clip views.
• nurec-fixer — clean up artifacts in already-rendered frames.

Use this index when the user mentions NuRec / NRE / 3DGUT / USDZ / "render this clip" / "convert this bag" / "extract objects" but the right sub-skill is not yet obvious. Always read this first.

Do NOT use this index when:

• The right sub-skill is already obvious (open it directly).
• The task is not a NuRec task (this skill will not help).
• Hands-on implementation steps are needed — defer to the sub-skill this index points at.

Purpose

This skill exists so an agent never has to guess which NuRec-family skill to read next. It is a hand-curated router for the five-skill NuRec family and nothing else.

Use cases this skill is built for:

• Disambiguate a NuRec request. The user says "render this clip", "convert this bag", "fix these frames", or "extract this car" and you need to pick the one sibling skill that owns that verb.
• Bootstrap a multi-stage pipeline. The user's goal needs two or three siblings in a fixed order (convert → train → render, dataset → render, harvest → insert, render → harmonize). This index points you at the right starting sibling and the matching workflow A–G in references/workflows.md.
• Translate NuRec jargon for a beginner. NuRec vs NRE, USDZ vs NCore V4, 3DGUT vs 3DGRT, NRE's built-in Fixer vs the standalone DiffusionHarmonizer — see Easy mix-ups.
• Locate a sibling skill that is not on disk. Defer to references/discovery.md instead of guessing a path.
• Plan disk cleanup across the family. A full NuRec workflow can leave 150 GB+ behind; defer to references/teardown.md for the documented order.

Use cases this skill is explicitly NOT built for:

• Running any container, training job, rendering job, conversion, dataset download, server, or teardown — always handed off to the sibling.
• Routing tasks outside the NuRec family (Omniverse, Isaac Sim, CARLA, Cosmos-* training, generic Hugging Face downloads).
• Discovering newly-added sibling skills automatically — the catalogue is hand-curated and must be edited by hand (see Keeping this index up to date).

Instructions

Follow these steps when answering a NuRec-shaped question:

1. Classify the request. Read the user's goal and match it to a row in Pick a skill. Multi-stage tasks (convert + train, dataset + render) usually start with ncore or physical-ai-datasets and then hand off to nre.
2. Open exactly one sibling skill next. Refer to it by name: (e.g. nre), not by file path — names are portable across runtimes. If the sibling is not on disk locally, follow references/discovery.md.
3. Defer execution to the sibling. This index is read-only and describes routing only; it never runs containers, training, rendering, conversion, or downloads itself.
4. For multi-step pipelines, walk a workflow in order. Pick a workflow ID (A–G) from references/workflows.md and open the named siblings one at a time, in the listed order. Do not collapse steps from that file into this index.
5. On disk cleanup, follow references/teardown.md. A complete NuRec workflow can leave 150 GB+ on disk; each sibling owns its own teardown.
6. Never echo secrets (NGC_API_KEY, HF_TOKEN). Use each sibling's scripts/validate_setup.py when present, or hf auth whoami. See the "Secrets" block in references/teardown.md.

What is NuRec?

NuRec (NVIDIA Omniverse Neural Reconstruction) takes a recording from cameras and LiDAR — usually from a self-driving car or a robot — and turns it into a 3D scene that can be re-rendered from any angle.

Names that appear often:

• NRE — "Neural Reconstruction Engine", the program that does the actual training and rendering. NuRec is the product name; NRE is the engine inside it.
• USDZ — the file format the trained scene is saved in. A zip archive that Omniverse, Isaac Sim, and CARLA know how to open.
• NCore V4 — the input format. Raw recordings must be converted into NCore V4 before NRE can consume them.
• 3DGUT / 3DGRT — two flavours of 3D Gaussian Splatting that NRE uses internally. Most users never pick between them; the default Hydra recipe handles it.

A typical NuRec project has three stages:

1. Get the input — convert a recording into NCore V4, or download a pre-converted dataset from Hugging Face.
2. Train the reconstruction — feed NCore V4 to NRE; out comes a USDZ file.
3. Render new views — point NRE at the USDZ to render images, videos, or LiDAR sweeps from any camera angle.

Some projects skip step 2 entirely by downloading a USDZ that NVIDIA has already trained.

Pick a skill

Match the user's goal in the left column, then open the skill on the right. Arrows mean "do these in order".

Goal	Skill to read
Find or download a NuRec dataset NVIDIA has published	physical-ai-datasets
Convert camera / LiDAR / radar / depth / stereo into NCore V4	ncore
Write a new converter for a sensor setup not yet supported (drone, RGB-D, ROS 2 bag, COLMAP, …)	ncore
Train a 3D reconstruction from an NCore clip	ncore → nre
Generate the extra inputs NRE needs (segmentation, depth, ego mask)	nre (via nre-tools container)
Render a USDZ along the original camera positions	nre
Render at full resolution / highest quality	nre ("Quality presets" inside that skill)
Render along a shifted trajectory	nre
Render through a server so a 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 an NVIDIA-built driving scene	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 rendered frames (ghosting, floaters, flickering, inserted-object lighting)	nurec-fixer, inside

For multi-step pipelines, see references/workflows.md (workflows A–G).

The skills in this folder

Open siblings by their Name — that is the canonical identifier. The Folder column is just where the skill lives in this repo if it has been cloned locally.

Name	Folder	What it does
physical-ai-datasets	physical-ai-datasets/	Catalog and download recipes for every NVIDIA Physical AI dataset on Hugging Face (driving, robotics, manipulation, NuRec scenes, benchmarks).
ncore	ncore/	Converts any sensor recording into NCore V4. Also covers writing a new converter.
nre	nre/	The Neural Reconstruction Engine itself. Trains reconstructions, renders frames, exports meshes / point clouds / depth, edits actors, runs the gRPC server, browses results, evaluates quality.
asset-harvester	asset-harvester/	Apache-2.0 pipeline that extracts individual 3D objects from sparse driving-clip views as .ply Gaussian splats plus metadata.
nurec-fixer	nurec-fixer/	Standalone DiffusionHarmonizer workflow that cleans up rendered frames, harmonizes inserted actors, evaluates PSNR/LPIPS, and optionally fine-tunes the model.

Easy mix-ups

These pairs sound similar but are different things. When in doubt, come back here.

• NuRec vs NRE. NuRec is the product name; NRE is the engine inside it. Both map to the same skill: nre.
• NRE's built-in Fixer vs standalone DiffusionHarmonizer. --enable-difix inside nre is an inline NRE rendering feature. The nurec-fixer skill covers the standalone public DiffusionHarmonizer release (code at NVIDIA/harmonizer, model at nvidia/DiffusionHarmonizer) for frames already on disk, paired evaluation, and fine-tuning. Do not assume these two paths share cache layout or weights unless the NRE tag's own docs say so.
• ncore vs nre. They run in order, never as alternatives. ncore produces the input format; nre reads it.
• asset-harvester vs nre's export-external-assets. Asset Harvester produces the per-object .ply files. nre's export-external-assets packages them into a USDZ. Always Asset Harvester first.
• Cosmos-Drive-Dreams vs PhysicalAI-Autonomous-Vehicles-NuRec. Both are AV datasets on Hugging Face; both are managed by physical-ai-datasets. Cosmos-Drive-Dreams is synthetic weather-augmented video (CC-BY-4.0). The NuRec dataset is real driving scenes turned into renderable USDZs (gated AV License).

Prerequisites

This index is read-only and needs no tooling. The real prerequisites live in each sibling skill:

Sibling	Hard requirements
ncore	Python 3.10+, pip install nvidia-ncore; source data on disk
nre	Linux x86_64, NVIDIA GPU (Ampere+, ≥24 GB VRAM), Docker 23+, NVIDIA Container Toolkit, NGC_API_KEY
asset-harvester	Linux + conda, NVIDIA driver ≥570, ~16 GB VRAM, HF_TOKEN
nurec-fixer	Linux, NVIDIA GPU (Ampere+), Docker, NVIDIA Container Toolkit, HF_TOKEN; NGC_API_KEY may be needed for nvcr.io pulls
physical-ai-datasets	Python + huggingface_hub, HF_TOKEN (gated datasets need license acceptance)

Each sibling skill ships scripts/validate_setup.py (where applicable) — run it before invoking the workflow. Never echo secret env vars; see references/teardown.md.

Examples

Concrete routing examples. The user prompt is on the left; the correct action this index should take is on the right.

Example 1 — single-skill routing

User: "I have a Waymo Open recording. How do I get it into a format NRE accepts?"

• Match the row "Convert camera / LiDAR / radar / depth / stereo into NCore V4" in Pick a skill.
• Open the ncore skill next; do not run any commands from this index.

Example 2 — multi-stage pipeline

User: "I have a driving clip. I want to train NuRec and then render a new camera trajectory through it."

• Match "Train a 3D reconstruction from an NCore clip" → ncore → nre.
• Cross-check workflow A in references/workflows.md.
• Open ncore first, then nre, in that order.

Example 3 — skip training, just render

User: "Can I just see NuRec working on a scene NVIDIA already built?"

• Match "Skip training and just render an NVIDIA-built driving scene" → physical-ai-datasets → nre.
• Cross-check workflow B.
• Open physical-ai-datasets first to download one scene (~1.5–2 GB), then nre to render.

Example 4 — actor editing

User: "I want to add a pedestrian to this NuRec scene."

• Match "Add, remove, or replace cars / pedestrians" → asset-harvester → nre.
• Cross-check workflow D.
• Confirm the original NCore clip is on disk first (Asset Harvester needs it), then open asset-harvester, then nre with serve-grpc --enable-editing-actors.

Example 5 — ambiguous "fix it" request

User: "My rendered frames look fuzzy with weird floaters. Can you clean them up?"

• Match the "Clean up rendered frames" row. Two valid paths:
  • Quick path: --enable-difix inside nre if the user is already rendering through NRE.
  • Standalone path: nurec-fixer for already-rendered frames on disk or for paired evaluation / fine-tuning.
• Ask which interface the user wants, then open the matching skill. See workflow E for details.

Example 6 — sibling skill not on disk

User: "Where do I find the nre skill? It is not in my repo."

• Do not guess a path. Follow references/discovery.md: look under .agents/skills/nre/SKILL.md, then .claude/skills/nre/, then .cursor/skills/nre/, then ~/.cursor/skills/nre/, and as a last resort clone https://github.com/NVIDIA/nurec-skills.

Limitations

• Routes only; never runs pipelines. For hands-on steps, open the sibling skill this index points to.
• Hand-curated catalogue. A newly-added sibling skill is not discoverable here until someone updates Pick a skill.
• Names are portable, paths are not. Cross-skill links assume the canonical layout (.agents/skills/<name>/SKILL.md). In a different runtime layout, prefer name-based skill resolution over file paths.
• No Omniverse / Isaac Sim integration steps — those live in upstream Omniverse / Isaac docs, not in the NuRec skill family.

Troubleshooting

Symptom	Likely cause	Resolution
Agent picked the wrong sibling skill	The user's task spans multiple stages (e.g. convert + train)	Re-read Pick a skill and follow the arrows; multi-stage tasks usually start with ncore or physical-ai-datasets, then hand off to nre.
Sibling skill not found on disk	The host repo only has the index	Follow references/discovery.md.
Stale link to ncore-data-conversion	Older snapshots used that name; the skill is now ncore	Update the link to ncore.
User wants to delete disk artifacts	NuRec workflow caches grow large	Walk references/teardown.md in the documented order.
User asks "should I retrain or just clean up frames?"	Conflating reconstruction vs post-processing	Retrain → nre; clean already-rendered frames → nurec-fixer.

References

Detailed material that this index intentionally keeps out of the hot path. Read only when the matching section above points there.

• references/workflows.md — full step-by-step multi-skill workflows A–G.
• references/teardown.md — disk cleanup order across all five siblings plus secrets-handling policy.
• references/discovery.md — how to locate or fetch a sibling skill that is not already on disk; thin-local-skill policy and upstream links.

Keeping this index up to date

This index is hand-curated — it groups skills by what users want to do, not alphabetically. There is no generator script; edit this SKILL.md by hand whenever the sibling set changes.

When a new sibling skill is added or a use case shifts:

1. Add a row to Pick a skill for the new use case.
2. Add a row to The skills in this folder.
3. If the new skill changes a multi-step pipeline, update references/workflows.md.
4. Confirm the sibling's metadata.upstream field still points at the canonical upstream repo or container.

Otherwise the index will quietly drift and beginners will end up reading the wrong skill.