In3D
Transform mobile videos into photorealistic, fully-rigged 3D avatars in under 60 seconds.
Turn 2D Video into Hyper-Realistic 3D Assets with Advanced Neural Radiance Fields and Gaussian Splatting.
NeRF Pro, the professional-grade tier of the KIRI Engine ecosystem, represents the 2026 benchmark for neural reconstruction. The platform integrates Neural Radiance Fields (NeRF) with high-efficiency 3D Gaussian Splatting (3DGS) to solve the 'uncanny valley' of 3D scanning. Unlike traditional photogrammetry which struggles with reflective or thin surfaces, NeRF Pro uses a volumetric approach to calculate density and color in 3D space, allowing for the capture of glass, metal, and complex lighting environments. In 2026, the tool has shifted toward a hybrid processing model, utilizing local edge computing for initial alignment and cloud-based H100 clusters for final neural training. This architecture enables the generation of high-fidelity meshes and splats within minutes rather than hours. Positioned for VFX artists, e-commerce giants, and digital twin engineers, NeRF Pro provides a robust pipeline for exporting production-ready assets with automated PBR (Physically Based Rendering) texture baking and LOD (Level of Detail) generation, ensuring compatibility across Unreal Engine 5, Unity, and Omniverse.
Combines the continuous volume density of NeRF with the point-based rasterization speed of 3D Gaussian Splatting.
Transform mobile videos into photorealistic, fully-rigged 3D avatars in under 60 seconds.
The universal identity layer for interoperable 3D avatars across the open metaverse.
The industry-standard platform for high-fidelity 3D digital human creation and animation-ready rigging.
Accelerate 3D production cycles with natural language command execution and AI-assisted Python scripting.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses Segment Anything Model (SAM) integration to automatically isolate subjects from busy backgrounds during training.
Neural networks estimate roughness, metallic, and normal maps from raw image data.
Allows for the synchronization of multiple video feeds for high-speed volumetric capture.
Compresses high-resolution textures using neural weights to reduce file size without losing visual fidelity.
Generates five levels of detail (LOD0-LOD4) during the mesh extraction process.
Separates diffuse color from specular highlights using the radiance field decomposition.
Creating high-fidelity 3D models of reflective products like jewelry or electronics is traditionally impossible with standard photogrammetry.
Registry Updated:2/7/2026
Reducing the cost of high-end photogrammetry rigs for indie film studios.
Preserving architectural details that are difficult to reach or have complex lighting.