Global Objects: Production-Ready Photoreal 3D Assets via Azure and NVIDIA

Global Objects is using a mix of cinema-grade capture rigs, cloud-scale compute, and NVIDIA-accelerated tooling to assemble what it calls a marketplace-sized library of photoreal, production-ready 3D objects—digital twins of real props, products, and environments intended for film, XR, simulation, and AI training. The company’s workflow combines high-fidelity capture (sub-millimeter precision), automated reconstruction and AI cleanup, encrypted cloud storage, and developer-facing asset delivery—backed by Microsoft Azure’s global footprint and NVIDIA’s GPU and Omniverse ecosystem. The result is a practical production pipeline that promises major cost and time savings for studios and enterprises, but it also raises important technical, legal, and ethical questions about scale, provenance, and how “the world’s largest” 3D object library should be measured and governed.

Background / Overview​

Global Objects appears in Microsoft’s Catalyst-style storytelling about startups that marry cloud-native agility with accelerated computing. The company is positioned as a media-and-entertainment-first data platform that captures real-world items and places, converts them into photoreal 3D assets, and stores them for use in virtual production, generative AI training, simulation, and interactive experiences. Microsoft’s write-up of Global Objects highlights their millimeter-level capture precision, the usage of Azure for global storage and scale, and concrete customer results—production savings in documentary and scripted projects—while Global Objects’ own product pages describe a modular “GO” ecosystem (GO Wave, GO Float, GO Vault, GO Deep) for capture, processing, secure storage, and AI-ready datasets.
This initiative is presented inside a larger program that pairs Microsoft Azure with NVIDIA hardware and developer frameworks. Microsoft’s startup and Azure-facing materials underscore NVIDIA’s role in providing accelerated compute for training and inference, and NVIDIA’s Omniverse and “Physical AI” tooling are being touted as the enabling fabric for large-scale world building, simulation, and asset annotation—exactly the sort of tooling companies like Global Objects need to move from raw scan to production-ready 3D asset at scale.

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How Global Objects captures and builds photoreal 3D assets​

The hardware: GO Wave, GO Float and multi-modal capture​

Global Objects describes a capture stack that mixes photogrammetry, LiDAR-style point capture, and studio-grade imaging to produce extremely dense, texture-rich models. Their marketing materials position GO Wave as a “desktop-size” rig capable of sub-millimeter accuracy, while GO Float and other mobile systems extend capture capability to larger props and real-world locations. The company emphasizes multi-modal capture so geometry (shape) and appearance (albedo, specularity, microdetail) are preserved.
Why that matters: photogrammetry alone can generate excellent detail but often struggles with reflective surfaces or tight occlusions; combining high-resolution imaging with additional depth sensing and controlled capture rigs reduces reconstruction noise and the amount of manual cleanup artists must perform.

The software pipeline: photogrammetry, reconstruction, and AI cleanup​

Once images and depth captures are collected, those raw inputs enter an automated pipeline:

• Offload and ingest to cloud storage near the capture location (minimizes upload latency).
• Photogrammetric alignment and dense reconstruction to produce geometry and raw textures.
• AI-based hole filling, texture correction, denoising, and material separation to make assets engine-ready.
• Artist finishing—retopology, UV optimization, LOD creation, and real-time format conversion (Unreal/Unity/ USD / Gaussian splats).
• Encryption, provenance tagging, and publication to a marketplace or private vault.

Global Objects specifically promotes "GO Splats" (Gaussian splats for real-time rendering) and output formats optimized for game and XR engines. Gaussian splatting and other point-based representations are gaining traction because they let developers show near-photoreal detail with reduced polygon budgets—critical for real-time use.

Storage, security and IP controls: GO Vault​

Global Objects stores assets in a managed, encrypted repository it calls GO Vault. The platform is described as “enterprise-ready” and built on Microsoft Azure; features cited include encryption-at-rest, access controls, and embedded DRM and provenance metadata so buyers and creators can track rights and usage. These controls are central to their value proposition: photoreal scans are IP-rich (props, branded items, heritage objects), and buyers in film or enterprise require clear rights and audit trails.

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The cloud and compute backbone: why Azure matters​

Global Objects explicitly chose Microsoft Azure for three core reasons: global datacenter footprint, scalable GPU/VM options for rendering and reconstruction, and enterprise security and compliance. Azure enables the company to accept captures in London, Los Angeles, or anywhere else, then make the data simultaneously available for artists and automated pipelines across regions via network peering and global Blob storage. For compute, Global Objects uses Azure NV-series and other GPU-accelerated VMs to run rendering and reconstruction workloads on demand—scaling from small test runs to production render farms when required. Microsoft’s case study lays out specific project outcomes and the pipeline details that made cross-region collaboration practical.
The operational implication is straightforward: high-throughput photogrammetry is an I/O and GPU-heavy workload. Cloud VMs let Global Objects avoid a fixed capital expense and elastically match compute to demand—particularly valuable when a single location capture generates terabytes of imagery and point clouds that must be stitched into production-quality assets in short time windows.

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NVIDIA’s role: GPUs, Omniverse and “physical AI”​

Global Objects lists participation in NVIDIA’s Inception program and builds workflows that benefit from NVIDIA compute, including accelerated reconstruction and Omniverse-compatible asset formats. NVIDIA’s recent product and platform pushes—Omniverse for collaborative world building, NIM microservices for USD/asset search and Edify/SimReady models for automating physical attribute labeling—map directly onto the steps needed to produce scalable, simulation-ready 3D libraries. In other words, NVIDIA provides hardware acceleration and software primitives that reduce the time from raw capture to simulation-ready, physically-labeled assets.
NVIDIA’s public messaging around generative physical AI—tools that can automatically add physical attributes and metadata to assets—addresses a real pain point. Manual labeling of physics, proper material IDs, and simulation-ready parameters is hugely time-consuming; models that can infer or propose such metadata (which Omniverse and related tooling seek to do) would dramatically speed asset publication for robotics, simulation, and synthetic data pipelines.

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Concrete outcomes: production, costs, and business cases​

Microsoft’s write-up on Global Objects highlights real client outcomes: on certain projects the studio avoided costly reshoots, saved days of on-set time, and cut multi-day shoot budgets by six-figure amounts. Global Objects’ technology can substitute for physical props or location visits during post-production—allowing directors and VFX teams to make late creative decisions without expensive location rework or reshoots. Microsoft’s case study quotes customer savings and names projects to illustrate the size of the impact.
Benefits list:

• Faster turnaround on visual effects and set extensions.
• Lower travel and logistical cost for location-dependent shoots.
• Reproducible, licensed 3D props for ongoing production and marketing.
• Reusable datasets that accumulate value as they support more downstream generative-AI and XR scenarios.

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Checking the “world’s largest” claim: context and competition​

Global Objects markets the ambition of building the world’s largest photoreal 3D object library—and that’s a bold positioning. But claims of scale invite immediate comparison with academic and open datasets that already boast millions of 3D models or millions of rendered views.

• Objaverse-XL is a public dataset claimed to contain 10M+ 3D objects aggregated from many sources and has been used extensively for 3D research and generative model training. That dataset is one benchmark for scale in academic and research communities.
• Public academic efforts like uCO3D and OmniObject3D focus on diversity, high-quality real-scanned objects, and research-grade annotations—each setting different standards for what “largest” might mean (count of objects, photoreal fidelity, annotation quality, coverage of categories).

Bottom line: “largest” depends on how you measure—number of raw models, production-ready photoreal assets, engine-optimized assets with rights-cleared licensing, or datasets with physical attributes suitable for robotics/simulation. Global Objects’ marketplace and enterprise focus aim for production utility (engine-ready, rights-cleared, provenance-tracked assets) rather than raw object count alone, which is an important distinction when comparing to academic datasets. Any unqualified “world’s largest” claim should therefore be read as marketing shorthand unless accompanied by transparent metrics (asset count + quality definition + licensing coverage).

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Strengths: why this pipeline matters right now​

• Production-first focus: Global Objects builds assets that are immediately usable in Unreal/Unity/Omniverse pipelines—something VFX houses, game studios, and virtual production teams can plug into quickly. This is a significant time-to-value advantage compared with raw, academic scan repositories.
• Enterprise-grade security and provenance: GO Vault and Azure-backed encryption plus embedded DRM solve an urgent problem for studios and brands worried about IP leakage and rights misuse. Turning a scanned shoe or branded prop into a usable commercial asset requires careful rights management—Global Objects explicitly addresses this.
• Elastic cloud compute and global collaboration: Azure’s ability to shift workloads between datacenters and offer GPU-ready VMs means teams in different continents can work on the same asset without moving terabytes across slow links. That operational flexibility equals faster iterations and lower idle costs.
• NVIDIA acceleration and Omniverse compatibility: GPU acceleration reduces reconstruction and rendering time; Omniverse-style USD interoperability and automatic labeling tools accelerate asset readiness for simulation and robotics. These platform alignments are practical, not just marketing.

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Risks and caveats: what to watch closely​

1) “Photoreal” is expensive and sometimes unnecessary​

High-fidelity capture costs time and money. Not every game or XR experience needs sub-millimeter detail. Publishers and creators should weigh fidelity vs. cost: sometimes procedurally generated or artist-modeled assets are the better choice for performance-constrained projects.

2) Claims of scale require transparent metrics​

“World’s largest” needs a published metric: how many unique assets, what percent are engine-optimized, what licensing and territorial rights are included, and how many are tagged for physical simulation. Without that, comparisons to academic datasets (Objaverse, OmniObject3D, uCO3D) are apples-to-oranges.

3) Data provenance, IP and rights management remain complex​

Even with vaults and DRM, provenance is not a solved problem. Who owns the scan of a museum artifact? What happens when a scanned prop looks like a trademarked design or a fashion product? Buyers and publishers must insist on explicit, auditable rights and limitations embedded in asset metadata. Global Objects emphasizes DRM and provenance—but rights enforcement and cross-border IP law are still non-trivial.

4) Bias, representativeness and dataset safety​

Large-scale datasets often replicate biases and omissions from the real world. For generative-AI training, the geographic, cultural, and product distribution of scans matters: an asset catalog skewed to Western consumer objects will underrepresent non-Western artifacts. Responsible dataset curation, inclusion metrics, and explicit documentation are necessary.

5) Environmental and cost footprint​

Mass-scale photogrammetry and GPU compute require energy. Companies should disclose typical compute-hours per asset, energy efficiency steps, and cloud-region choices (to leverage lower-carbon grids) so buyers can compare environmental costs as well as price. Some of these factors are addressed in cloud provider sustainability stories, but end-to-end transparency is still relatively rare.

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Practical guidance for buyers, studios, and developers​

• Ask for metadata and provenance: require that any purchased asset include a detailed manifest—capture date, device profiles, rights holder, permitted uses, and any redactions or synthetic edits applied.
• Define fidelity targets: set concrete LOD and texture-size targets for assets you plan to deploy so you don’t overpay for unnecessary fidelity.
• Test sample workflows end-to-end: run a trial asset through your engine, lighting pipeline, and render budget before committing to large purchases or custom scanning orders.
• Consider hybrid approaches: combine Global Objects’ photoreal assets for hero shots with procedurally generated or artist-optimized assets for background or interactive content to balance cost and performance.
• Demand safety and inclusion metrics if using assets to train generative models: ask providers for documentation showing the regional, category, and source distribution of assets used in any training sets.

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How this fits into the broader 3D data landscape​

Academic and open-science communities are building massive 3D corpora—Objaverse, OmniObject3D, uCO3D and HM3D among them—and each dataset serves different use cases: research, embodied AI, simulation, or large-scale generative modeling. Industry players like Global Objects aim at commercial-grade, rights-cleared, production-ready assets. Both approaches are complementary: open datasets accelerate research and model development, while enterprise libraries address the production, licensing, and support needs of studios and businesses. Anyone comparing platforms should evaluate both the dataset’s purpose and its packaging.

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Looking ahead: product roadmap and ecosystem signals​

Global Objects is positioning a marketplace (GO Marketplace) and an ecosystem that stitches capture hardware, asset management, and AI training datasets into a single commercial product. Microsoft’s startup programs and Azure credits, plus NVIDIA’s Inception and Omniverse ecosystems, form a favorable platform for rapid iteration and scale. But market leadership will depend on transparent metrics (what “largest” actually means), robust rights management, and affordable pricing models that make commercial adoption straightforward for studios and game developers.
A few signals to watch:

• Marketplace beta cadence and published catalog metrics (counts, categories, licensing terms).
• Published documentation of compute-costs per asset and sustainable-cloud commitments.
• Third-party audits of provenance and rights metadata.
• Interoperability with major engines (USD/Omniverse, Unreal, Unity) and file formats that matter to production pipelines.

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Conclusion​

Global Objects is building a production-focused, cloud-native pipeline for capturing, processing, and distributing photoreal 3D assets—backed by Microsoft Azure for global scale and NVIDIA for hardware and interoperability. The commercial promise is clear: studios and enterprises can reduce real-world logistics and cost while increasing creative flexibility. At the same time, legitimate questions remain about claims of being “the world’s largest” library, the precise metrics behind that claim, and how the industry will address IP provenance, dataset bias, and environmental cost at scale. Verified public documentation from Microsoft and Global Objects describes the technical stack, business value, and early customer wins; independent academic datasets illustrate the intense competition and diversity of approaches in the 3D-data space. Buyers and creators should evaluate catalogs on the basis of fidelity, rights, provenance, and engineering readiness—not only headline asset counts.

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Bold advancements in capture hardware and accelerated cloud compute are making photoreal 3D libraries practical for production use for the first time. What remains is the industry-level discipline: transparent metrics, enforceable rights, and consistent documentation so that creators, engineers, and legal teams can adopt these assets with confidence. When that discipline meets technology, the value of digital twins—across entertainment, simulation, robotics, and AI—will be unlocked at real scale.

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Source: YouTube