Feed-Forward 3D Reconstruction#
Unlike diffusion-based methods (iterative sampling), feed-forward models use direct mapping:
- Architecture: Encoder (ViT for images/text) + Decoder (for 3D params).
- Inputs: Text prompts, single/multi-view images, videos.
- Outputs:
- Dense point clouds (millions of points).
- Multi-view depth maps and camera intrinsics.
- Surface normals for lighting.
- 3D Gaussian Splatting (3DGS): Efficient radiance fields for novel view synthesis.
- Advantages: Deterministic, fast (sub-second inference), no training needed per scene.
Multi-Modal Generation#
- Text-to-3D: Describe scene; model infers geometry (e.g., "A futuristic cityscape").
- Image-to-3D: Single-view reconstruction with depth estimation.
- Video-to-3D: Extract temporal consistency for dynamic assets.
- Hybrid: Combine inputs for guided generation (e.g., image + text for styled worlds).
Physically-Based Rendering (PBR) Integration#
For simulation-ready assets:
- Materials: Albedo, roughness, metallic maps.
- Geometry Accuracy: Watertight meshes with UV unwrapping.
- Textures: Aligned and high-res for realism.
Key Innovation: Universal Reconstruction – Single model handles all modalities, outputting compatible formats (e.g., OBJ, GLTF).
Gaussian Splatting for 3D#
3DGS represents scenes as Gaussians (position, scale, opacity, color):
- Efficiency: Rasterize in real-time vs. NeRF's ray marching.
- Quality: Photorealistic novel views.
- Training: Optimize per scene or use pre-trained for zero-shot.