3D reconstruction of insects: an improved multifocus stacking and an evaluation of learning-based MVS approaches

3D reconstruction of insects from photographs is a challenging task as it requires to tackle several problems such as strong out-of-focus areas in macro-photography, thin structures (insect legs and hairs), flat-colored surfaces (insects shells), non-Lambertian (shells specularities) and even translucent surfaces (wings). In this work, we first present a new lens-based image registration technique for accurate multi-focus stacking suitable for 3D reconstruction purposes while other methods create in-focus images for viewing purpose only. We then evaluate and compare the classical Multi-View-Stereo (MVS) reconstruction pipeline for small and complex objects with recent deep learning-based reconstruction methods such as the Neural Radiance Fields (NeRF) and the Neural Sparse Voxel Fields (NSVF). We present an assessment of different sources of errors for the considered methods. The results are compared both quantitatively and qualitatively across the different methods. From this analysis we present a series of practical guidelines for addressing the common issues of the reconstruction of small objects under challenging conditions.