Aego
High-Fidelity 3D Reconstruction from 2D Photosets
Aego transforms ordinary 2D photosets into production-grade 3D digital twins enriched with condition metadata. The output is a compact glTF model enriched with condition layers such as surface normals, depth, and defect masks. These models are not static replicas but dynamic, condition-aware representations that support simulation, inspection, and long-term monitoring. Within the QuantumSpace ecosystem, Aego provides the geometric and material counterpart to visual and semantic analytics, bridging empirical evidence with scalable deployment.
How It Works
Multi-view Capture Orchestration
The process begins with structured acquisition. Aego guides image capture strategies, such as angles, baselines, lighting variation, while calibrating optical parameters (intrinsics/extrinsics) to guarantee dataset consistency and reconstruction readiness.
Neural Reconstruction & Inverse Rendering
By combining principles of photogrammetry with neural inverse rendering, Aego estimates both geometry and material properties with scientific accuracy. Depth, reflectance, and surface normals are refined for realism and measurement, enabling defect detection and surface fidelity analysis. GPU acceleration ensures speed without compromising rigor.
Packaging & Delivery
Completed models are delivered as compact, interoperable glTF assets, enriched with condition layers and paired with lightweight viewers and APIs. This ensures reconstructed objects can be seamlessly inspected, compared over time, or integrated into AR/WebXR workflows.
Key Features
Condition-Enriched Digital Twins
Aego produces 3D assets in glTF enriched with measurable metadata, such as depth, normals, defect masks, ensuring not only visual fidelity but actionable diagnostic value for inspection, monitoring, and comparative analysis.
Evidence-Preserving Optimization
Advanced decimation and tiling strategies reduce file size for efficient rendering and distribution while safeguarding critical evidence. Fine textures, micro-defects, and diagnostic details remain intact, even in optimized outputs.
Immersive Deployment Across AR/WebXR
A lightweight SDK and direct export pipelines enable seamless integration into AR and WebXR environments. From immersive education to e-commerce engagement, reconstructed assets can be deployed interactively across platforms.
GPU-Accelerated Batch Pipelines
End-to-end GPU acceleration supports automated batch processing of large collections. Reconstruction, rendering, and denoising are parallelized at scale, enabling institutions to transition from individual objects to entire archives without loss of speed or fidelity.
Aego Applications
Inspection & Predictive Maintenance
Aego’s condition-aware 3D twins provide organizations with durable, measurable records of asset health. By embedding surface normals, depth, and defect layers, even subtle signs of wear or deformation become trackable over time. Instead of relying on periodic, manual inspections, organizations gain persistent, measurable digital records that evolve alongside the physical object. This enables predictive workflows where maintenance can be scheduled before failures occur, reducing downtime, extending asset life, and optimizing resource use across sectors such as aerospace, energy, and heavy industry.
Immersive Commerce & Marketing
For commercial applications, Aego elevates 3D asset production to production-grade fidelity while keeping assets lightweight enough for seamless distribution. Objects reconstructed from ordinary photosets are transformed into interactive digital experiences that can be embedded into websites, mobile apps, or immersive AR and WebXR platforms. These assets allow customers to explore products with photorealistic accuracy, rotating, zooming, or simulating material variations in real time. The result is faster time-to-market, lower costs, and more engaging product experiences that drive conversion and brand differentiation.
Conservation & Restoration
In heritage and museum settings, Aego creates diagnostic twins enriched with condition metadata, offering conservators powerful tools for analysis and intervention. Conservators can simulate “what-if” scenarios—testing potential restoration materials under virtual lighting, exploring how surfaces will react to aging, or documenting the state of fragile artifacts before and after intervention. Beyond restoration, Aego provides a permanent digital record of cultural assets, ensuring that their condition-aware digital twin remains preserved for study, replication, or public dissemination.
Synthetic Data Generation for Machine Learning
Aego is uniquely positioned to accelerate AI development by producing synthetic datasets with scientifically controlled variations. By altering lighting conditions, injecting synthetic defects, or simulating material aging, Recon generates labeled datasets that mirror real-world conditions with far greater diversity than traditional data collection allows. These datasets provide a scalable, cost-effective alternative where annotated real-world data is limited or impractical, supporting the training and validation of machine learning systems for inspection, defect detection, and predictive modeling.
Aego Results
Aego consistently achieves high performance in 3D reconstruction benchmarks, balancing visual fidelity with computational efficiency. Reprojection error serves as the primary measure of geometric accuracy, confirming the alignment between reconstructed models and their source photo datasets. Image-based metrics such as PSNR and SSIM validate the realism of rendered outputs against original captures, ensuring that models retain both detail and structural integrity.
At the same time, Recon optimizes asset size relative to fidelity, applying adaptive decimation and tiling strategies that reduce file weight while preserving diagnostic evidence such as surface defects or fine textures. This balance enables reconstructed models to remain portable for AR/WebXR deployment and scalable for enterprise workflows, without compromising on analytical or visual quality.