Scholarlink

<p>We present a variational method for reconstructing 2D simple manifolds from a given level set data using shape gradients. Starting from an initial triangulated surface (mesh), iteratively evolves the mesh by minimizing a shape energy functional that reflects local geometric properties of the surface. The evolution is performed via a gradient descent scheme, guiding the mesh toward accurate alignment with the object’s boundary while ensuring smoothness. This approach ensures both geometric fidelity and regularity of the reconstructed shape. The effectiveness of the method is demonstrated through experiments on various synthetic shapes and also for human brain white matter MRI T1 data.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Diffeomorphic Reconstruction of a 2D-Simple Non-Parametric Manifold Via Shape Gradient

  • 期刊论文

  • 发表日期：

    2026年5月15日

查看全文
  • Shafeequdheen Palengara,
  • Jyotiranjan Nayak,
  • Vijayakrishna Rowthu

摘要

We present a variational method for reconstructing 2D simple manifolds from a given level set data using shape gradients. Starting from an initial triangulated surface (mesh), iteratively evolves the mesh by minimizing a shape energy functional that reflects local geometric properties of the surface. The evolution is performed via a gradient descent scheme, guiding the mesh toward accurate alignment with the object’s boundary while ensuring smoothness. This approach ensures both geometric fidelity and regularity of the reconstructed shape. The effectiveness of the method is demonstrated through experiments on various synthetic shapes and also for human brain white matter MRI T1 data.