Abstract
Recent advances in neural representations, such as Neural Radiance Fields (NeRF) and Neural Implicit Surfaces (NeuS), enable accurate 3D reconstruction of objects and scenes. While these methods achieve good results, they face significant challenges when dealing with small objects, such as insects, which contain intricate geometric details, including delicate wing patterns, antennae, and translucent surfaces, of high biological significance. In this paper, we propose Dynamic Neural Adaptive Sampling and Computation (DNASC), a transformative enhancement to Neural Implicit Surfaces (NeuS) and High-Frequency NeuS (HF-NeuS), optimized for complex insect morphologies. DNASC leverages thin structure scores, derived from edge detection and multi-scale variance analysis, to highlight detail-rich regions. We introduce a novel hybrid ray sampling strategy that ensures comprehensive scene coverage while focusing on critical areas, complemented by a score-weighted color loss that prioritizes high-detail zones. Also, we propose an adaptive score refinement mechanism that dynamically adjusts sampling based on reconstruction errors, driving iterative improvement of the geometry reconstruction. Evaluated on a custom insects datasets of three spieces, By building upon existing methods such as NeuS and HF-NeuS, the proposed DNASC achieves superior performance in various quantitative metrics (PSNR, SSIM, LPIPS, EPI) and qualitative mesh quality. This work elevates neural rendering for entomological applications, with profound implications for taxonomic studies and digital archiving. Code available at: https://github.com/a93088428/3d-insect-reconstruction