Learning-based isosurface extraction methods have recently emerged as a
robust and efficient alternative to axiomatic techniques. However, the vast
majority of such approaches rely on supervised training with axiomatically
computed ground truths, thus potentially inheriting biases and data artifacts
of the corresponding axiomatic methods. Steering away from such dependencies,
we propose a self-supervised training scheme for the Neural Dual Contouring
meshing framework, resulting in our method: Self-Supervised Dual Contouring
(SDC). Instead of optimizing predicted mesh vertices with supervised training,
we use two novel self-supervised loss functions that encourage the consistency
between distances to the generated mesh up to the first order. Meshes
reconstructed by SDC surpass existing data-driven methods in capturing
intricate details while being more robust to possible irregularities in the
input. Furthermore, we use the same self-supervised training objective linking
inferred mesh and input SDF, to regularize the training process of Deep
Implicit Networks (DINs). We demonstrate that the resulting DINs produce
higher-quality implicit functions, ultimately leading to more accurate and
detail-preserving surfaces compared to prior baselines for different input
modalities. Finally, we demonstrate that our self-supervised losses improve
meshing performance in the single-view reconstruction task by enabling joint
training of predicted SDF and resulting output mesh. We open-source our code at
this https URL

基于学习的等值面提取方法是一种强大且高效的替代公理技术的新兴方法。然而，绝大多数这类方法依赖于带有公理计算出的真值标签的监督训练，因此会潜在地继承相应公理方法的偏见和数据伪现象。为了避免这种依赖性，我们提出了一种针对神经双轮廓网格化框架的自监督训练方案，即自监督双轮廓（SDC）。我们使用两种全新的自监督损失函数，而非通过监督训练优化预测的网格顶点，以鼓励生成的网格与距离的一阶一致性。SDC 重建的网格在捕捉细节方面优于现有的数据驱动方法，同时对输入的可能不规则性更具鲁棒性。此外，我们使用相同的自监督训练目标连接推断网格和输入 SDF，以规范 Deep Implicit Networks（DINs）的训练过程。我们证明由此得到的 DINs 生成更高质量的隐式函数，最终相比先前的基准方法更准确且保留了更多细节的曲面。最后，我们证明我们的自监督损失通过实现预测 SDF 和生成输出网格的联合训练，提高了单视角重建任务的网格化性能。我们以此 https URL 开源我们的代码。