• The Journal of the Korea Contents Association
• /
• v.20 no.6
• /
• pp.9-25
• /
• 2020

As laser scan and photogrammetry are extensively applied to 3D modeling, the Retopology has become a critically important part in the 3D modeling process. However, abundant time would be wasted if the wrong method for retopology is employed. This paper aims to select the most suitable method and software for retopology for different types of models so as to increase the effectiveness of 3D modeling. In this paper, retopology is divided into three types according to the existed software for retopology in the market: manual, automatic and wrapping type, all of which are investigated by their characteristics of retopology and software in which they are applied individually. Then case production is employed on Static Mesh Skeletal Mesh and Hard Surface Modeling by the above mentioned three methods. The advantages and disadvantages of the software in which the above three methods can be applied are summed up, and the manual type produces good results, the automatic type is fast, and the wrapping type requires a pre-existing base mesh and the most suitable method for retopology for each type of 3D models is demonstrated. This paper provides reference for retopology and increases the effectiveness of 3D modeling.

• Journal of the Korea Computer Graphics Society
• /
• v.29 no.4
• /
• pp.17-24
• /
• 2023

In computer graphics and computer vision research and its applications, various representations of 3D objects, such as point clouds, voxels, or triangular meshes, are used depending on the purpose. The need for animating characters using these representations is also growing. In a typical animation pipeline called skeletal animation, "skinning weight painting" is required to determine how joints influence a vertex on the character's skin. In this paper, we introduce a neural network for automatically performing skinning weight painting for characters represented in various formats. We utilize signed distance fields (SDF) to handle different representations and employ graph neural networks and multi-layer perceptrons to predict the skinning weights for a given point.