Reconstructing transparent objects with high fidelity presents significant challenges due to complex light refraction and reflection. Existing methods rely on intentionally designed patterns observed behind the transparent object to infer the correspondence between rays and the background, thereby improving the precision of the reconstruction. However, they are hindered by a refraction-tracing-based strategy that fails to reconstruct complex nested transparent objects and a tedious view-capture strategy relying on images captured from empirically determined viewpoints. To overcome these obstacles, we propose AHC-NeRF, an autonomous, high-quality neural SDF-based framework designed for reconstructing two-layer complex nested transparent objects. Firstly, our framework combines neural SDF with single-pixel imaging, a reflection-based method, which utilizes point-pair priors as guidance to achieve high-quality reconstruction of both the outer and inner surfaces. Secondly, we propose an adaptive single-pixel imaging method that achieves an acceleration of 1-2 orders of magnitude compared to vanilla single-pixel imaging for the acquisition of point-pair priors. Finally, we introduce a novel view-planning strategy that progressively identifies the viewpoints with the highest information gain throughout the optimization process, thereby achieving high-quality surface reconstruction. Extensive experimental results on both synthetic and real-world datasets demonstrate that AHC-NeRF outperforms state-of-the-art methods.
Cai et al. (Thu,) studied this question.