MZEN: Multi-Zoom Enhanced NeRF for 3-D Reconstruction with Unknown Camera Poses
It solves the challenge of capturing fine details like sub-micron defects in industrial settings, extending NeRF to real-world factory applications.
The paper tackles the problem of 3D reconstruction from multi-zoom image sets with unknown camera poses, which is crucial for industrial inspection, and proposes MZEN, a NeRF framework that boosts PSNR by up to 28%, SSIM by 10%, and reduces LPIPS by up to 222% across various scenes.
Neural Radiance Fields (NeRF) methods excel at 3D reconstruction from multiple 2D images, even those taken with unknown camera poses. However, they still miss the fine-detailed structures that matter in industrial inspection, e.g., detecting sub-micron defects on a production line or analyzing chips with Scanning Electron Microscopy (SEM). In these scenarios, the sensor resolution is fixed and compute budgets are tight, so the only way to expose fine structure is to add zoom-in images; yet, this breaks the multi-view consistency that pose-free NeRF training relies on. We propose Multi-Zoom Enhanced NeRF (MZEN), the first NeRF framework that natively handles multi-zoom image sets. MZEN (i) augments the pin-hole camera model with an explicit, learnable zoom scalar that scales the focal length, and (ii) introduces a novel pose strategy: wide-field images are solved first to establish a global metric frame, and zoom-in images are then pose-primed to the nearest wide-field counterpart via a zoom-consistent crop-and-match procedure before joint refinement. Across eight forward-facing scenes$\unicode{x2013}$synthetic TCAD models, real SEM of micro-structures, and BLEFF objects$\unicode{x2013}$MZEN consistently outperforms pose-free baselines and even high-resolution variants, boosting PSNR by up to $28 \%$, SSIM by $10 \%$, and reducing LPIPS by up to $222 \%$. MZEN, therefore, extends NeRF to real-world factory settings, preserving global accuracy while capturing the micron-level details essential for industrial inspection.