We introduce a normal-based modeling framework for bas-relief generation and stylization which is motivated by the recent advancement in this topic. Creating bas-relief from normal images has successfully facilitated bas-relief modeling in image space. However, the use of normal images in previous work is restricted to the cut-and-paste or blending operations of layers. These operations simply treat a normal vector as a pixel of a general color image. This paper is intended to extend normal-based methods by processing the normal image from a geometric perspective. Our method can not only generate a new normal image by combining various frequencies of existing normal images and details transferring, but also build bas-reliefs from a single RGB image and its edge-based sketch lines. In addition, we introduce an auxiliary function to represent a smooth base surface or generate a layered global shape. To integrate above considerations into our framework, we formulate the bas-relief generation as a variational problem which can be solved by a screened Poisson equation. One important advantage of our method is that it can generate more styles than previous methods and thus it expands the bas-relief shape space. We experimented our method on a range of normal images and it compares favorably to other popular classic and state-of-the-art methods.

我们引入了基于浮雕的生成和样式化的基于正常的建模框架，该框架受本主题最近的发展所推动。从正常图像创建浅浮雕已成功促进了图像空间中的浅浮雕建模。但是，在以前的工作中使用普通图像仅限于图层的剪切和粘贴或混合操作。这些操作仅将法线向量视为普通彩色图像的像素。本文旨在通过从几何角度处理法线图像来扩展基于法线的方法。我们的方法不仅可以通过组合现有正常图像的各种频率和细节传递来生成新的正常图像，还可以从单个RGB图像及其基于边缘的草图线构建浅浮雕。此外，我们引入了一个辅助函数来表示光滑的基础表面或生成分层的全局形状。为了将以上考虑因素整合到我们的框架中，我们将浅浮雕生成公式化为一个变分问题，可以通过筛选的泊松方程来解决。我们的方法的一个重要优点是，它可以比以前的方法生成更多样式，从而扩展了浅浮雕形状空间。我们在一系列正常图像上进行了实验，与其他流行的经典方法和先进方法相比，它具有优势。我们的方法的一个重要优点是，与以前的方法相比，它可以生成更多样式，因此可以扩展浅浮雕形状空间。我们在一系列正常图像上进行了实验，与其他流行的经典方法和最新方法相比，它具有优势。我们的方法的一个重要优点是，与以前的方法相比，它可以生成更多样式，因此可以扩展浅浮雕形状空间。我们在一系列正常图像上进行了实验，与其他流行的经典方法和最新方法相比，它具有优势。