Detail-Preserving 3D Shape Modeling from Raw Volumetric Dataset via Hessian-Constrained Local Implicit Surfaces Optimization

Massive routinely-acquired raw volumetric datasets are hard to be deeply exploited by cyber worlds related downstream applications due to the challenges in accurate and efficient shape modeling. This paper systematically advocates an interactive 3D shape modeling framework for raw volumetric datasets by iteratively optimizing Hessian-constrained local implicit surfaces. The key idea is to incorporate contour based interactive segmentation into the generalized local implicit surface reconstruction. Our framework allows a user to flexibly define derivative constraints up to the second order via intuitively placing contours on the cross sections of volumetric images and fine-tuning the eigenvector frame of Hessian matrix. It enables detail-preserving local implicit representation while combating certain difficulties due to ambiguous image regions, low-quality irregular data, close sheets, and massive coefficients involved extra computing burden. Moreover, we conduct extensive experiments on some volumetric images with blurry object boundaries, and make comprehensive, quantitative performance evaluation between our method and the state-of-the-art radial basis function based techniques. All the results demonstrate our method's advantages in the accuracy, detail-preserving, efficiency, and versatility of shape modeling.