nvidia-physical-ai-neural-reconstruction

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

الانتقال إلى التثبيت

المستودع: Pyfagorass/bookofspells
آخر نشاط في المصدر: ٩ يونيو ٢٠٢٦ في ٠٩:١٨
لغة SKILL.md المكتشفة: الإنجليزية
النجوم: ٢
التفرعات: ٠

خيارات التثبيت

يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.

npx skills add https://github.com/Pyfagorass/bookofspells --skill nvidia-physical-ai-neural-reconstruction

يبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.

تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.

مراجعة ملفات المصدر

اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.

مستكشف الملفات

11 ملفات

SKILL.md

تعليمات المصدر · معاينة للقراءة فقط

العودة إلى البحث العودة إلى التثبيت

the-grimoire

The index to a Book of Spells — a large, curated library of ready-made spells (skills in the SKILL.md format) gathered from many providers (Google, OpenAI, and more), covering cloud platforms, deployment, design tools, PDFs, browser automation, security, and much else. The spells are NOT preloaded — there may be thousands. Use this whenever the user asks to cast a spell, or for a spell to do something, or names a capability, provider, or tool you do not already have loaded (e.g. "cast a spell for Figma", "is there a spell for deploying?", "use the bigquery spell"), or whenever a task might be served by a specialised, ready-made spell. Casting a spell MEANS consulting this index to discover and load the right one on demand — do that first, rather than improvising. This is also the single entry point to the Book's own keystones: route here to AUTHOR a new spell (the-spellwright), to FIND or CONNECT an MCP server / external tool (the-emissary), or to INSTALL the Book into a conduit (the-tether).

2026-06-09

the-tether

How to install this Book of Spells into a conduit so its gathered skills become usable — by symlink, not by copy. Use whenever the user wants to install, set up, enable, add, or "load" the Book's spells into their agent; asks where skills go or how a conduit finds them; or asks how to keep the spells up to date. The companion to the-grimoire (which *finds* spells once they are installed): this one *shelves the Book* so the grimoire has something to find.

2026-06-09

the-emissary

Find and connect an MCP server — an "Emissary of the Pact" — when the agent needs a tool, service, API, or data source it does not already have. Use when the user asks for an MCP server, to connect to or integrate an external service (Notion, GitHub, Figma, a database, Slack, a SaaS API…), asks "is there an MCP for X", "add an MCP", or sets a task that needs live tools the agent lacks. This rite DISCOVERS a server from the official MCP Registry and reputable directories and PROPOSES its connection config for the user to approve — it NEVER installs, launches, or writes credentials on its own. The companion to the-grimoire: that finds spells (inert skills); this finds Emissaries (live tool-servers).

2026-06-09

عرض الكل من هذا المستودع

name	nvidia-physical-ai-neural-reconstruction
description	Router for NVIDIA NuRec/NRE: USDZ rendering, NCore conversion, 3DGS, gRPC sensor sim, PhysicalAI HF datasets. Do NOT use for SimReady or infra setup.
license	Apache-2.0
version	0.3.0
tools	["Read","Shell"]
compatibility	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`.
metadata	{"author":"NVIDIA Physical AI","tags":["physical-ai","nurec","neural-reconstruction","router","sensor-sim"],"upstream":{"repo":"https://github.com/NVIDIA/nurec-skills","branch":"main","skills_dir":".agents/skills/","skills_dir_alias":"skills/","index_skill":".agents/skills/SKILL.md","index_skill_name":"nurec-index","sibling_skills":["[Truncated]","[Truncated]","[Truncated]","[Truncated]","[Truncated]"]},"upstream_clone_path":"${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}/nurec-skills","upstream_override_env":"NUREC_SKILLS_UPSTREAM_ROOT"}

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, , , "where do I start with NuRec", "which NuRec skill should I use for X?".

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)

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.

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`)

nvidia-physical-ai-neural-reconstruction

خيارات التثبيت

مراجعة ملفات المصدر

Physical AI Neural Reconstruction (NuRec) Router

Purpose

When to Use

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

nvidia-physical-ai-neural-reconstruction

معلومات المصدر

خيارات التثبيت

مراجعة ملفات المصدر

المزيد من هذا المستودع

المزيد من هذا المستودع

Physical AI Neural Reconstruction (NuRec) Router

Purpose

When to Use

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