3D Scanning Pipeline for Assets
Production-grade video-to-3D pipeline supporting photogrammetry, NeRF, and Gaussian splatting. Automatic frame extraction, quality filtering, and mesh reconstruction. Post-processing includes hole filling, LOD generation, and PBR texture baking. Exports to GLTF, OpenUSD, and FBX. Achieves 60-80% file size reduction while maintaining quality.
⚡ Challenges
- •NeRF training time optimization
- •Automatic mesh cleanup
- •Multi-format export compatibility
✓ Outcomes
- ✓60-80% file size reduction
- ✓5x faster than manual workflow
- ✓Used for 50+ cultural heritage assets
📖 Full Details
This production-grade 3D scanning pipeline transforms video footage into optimized 3D assets suitable for web delivery, AR experiences, and industrial applications. The pipeline supports multiple reconstruction methods—traditional photogrammetry, Neural Radiance Fields (NeRF), and Gaussian splatting—selecting the optimal approach based on source material and output requirements.
The capture stage provides guidance for optimal video acquisition, including coverage patterns, lighting recommendations, and motion blur detection. Frames are extracted and filtered using computer vision techniques that identify and remove blurry, overexposed, or redundant images.
Photogrammetry processing uses Meshroom or RealityCapture depending on asset complexity, generating dense point clouds and textured meshes. For challenging subjects with reflective or transparent surfaces, NeRF-based reconstruction using Instant-NGP or Nerfstudio produces superior results by modeling view-dependent effects.
Post-processing includes automatic mesh cleanup—hole filling, smoothing, and decimation to target polygon counts. Texture baking generates optimized UV maps with PBR material channels (diffuse, normal, roughness). Multi-level LOD (Level of Detail) variants enable efficient web streaming with progressive loading.
The output stage supports multiple formats including GLTF (web), USD (industry standard), and FBX (game engines). OpenUSD export ensures compatibility with major DCC applications and enables composition into larger scenes. Automated compression reduces file sizes by 60-80% while maintaining visual quality.
The pipeline runs as containerized microservices with job queuing, enabling batch processing of multiple assets. A web interface provides progress monitoring and quality preview before final export.
