BladeNeRF: Exploiting camera constraints for NeRF in repetitive texture-less 3D reconstruction

Neural Radiance Fields (NeRFs) have demonstrated remarkable capabilities in photo-realistic 3D reconstruction. NeRFs often take as input posed images where the camera poses come from either off-the-shelf S\textit{f}M or online optimization together with NeRFs. However, we find that both strategies yield suboptimal results in recovering camera poses from images when encountering texture-less and repetitive patterns, particularly in aircraft engine inspection. To reconstruct photo-realistic 3D engine blades from images, we propose BladeNeRF, a new variant of NeRF model that incorporates camera constraints into learning and enables accurate pose learning. In addition, we propose to separate the blades in the foreground from the constant background, eliminating background artefacts and enhancing depth estimation accuracy. Experimental evaluations on synthetic data demonstrate the advantage of our model in precise camera pose estimation and high-fidelity 3D scene reconstruction compared to other NeRF variants