Rigging and Fabricating Creative Characters

创造力支持的造型技术常用于辅助普通用户的开放式造型过程.针对现有的大多数创造力支持的造型技术针对静止物体造型而设计,无法造型动态模型的问题,提出; 一种造型动态模型的技术,其造型结果是已蒙皮并可直接三维打印的模型.该技术分为模型进化与应用2个阶段.在模型进化阶段,用户从数据库内选择一组绑定的; 模型,迭代地产生一代代新模型,作为建议提示给用户,以激发灵感;在应用阶段,用户选择感兴趣的模型用于动画编辑与三维打印.实验结果表明,文中技术将造; 型、动画编辑与面向三维打印的模型分析集成至统一的框架,极大地帮助了用户的创意建模过程.Creative modeling techniques are commonly used to assist novice users in; open-ended 3D content creation. Most existing creative modeling methods; are mainly designed to model static objects only, not appropriate to; model dynamic models. We present a method for modeling dynamic creative; models which are rigged and fabricatable. There are two stages: models; evolution and application. During the models evolution stage, the users; select a small set of skinned watertight objects, our technique; iteratively synthesizes new creative characters for users to explore.; During the application stage, the users can choose those of interest for; animation or fabrication directly. Experiments demonstrate that the; proposed technique unifies modeling, animation and fabrication together,; facilitating the creative design process.国家自然科学基金; 国家科技支撑计划课

Deep Spherical Panoramic Representation for 3D Shape Recognition

三维形状识别是近年来较为热门的研究方向,针对其中的三维模型形状的表达方法和识别问题,提出一种多分支卷积神经网络下的三维模型识别方法.该方法通过对; 三维模型进行球面深度投影得到球面全景图;为了提高识别精度,将每个模型的球面全景图从多个角度展开,创建多幅平面图像作为识别系统的输入;识别系统使用; 多分支的卷积神经网络,并将多幅全景图进行整合分析,最终得到一个三维模型的识别结果.对三维模型进行分类和检索的实验结果表明,文中方法的识别效果优于; 近年来的前沿方法,对三维模型进行检索的准确度甚至超过了多视图识别方法.3D shape recognition is a hot topic in recent years. This paper proposed; a 3D model recognition method with multi-branch convolutional neural; network (CNN) to address the problems of 3D shape representation and; recognition. The inputs of the proposed method are spherical panoramas; by deep spherical projection of 3D models; to improve recognition; accuracy, the spherical panorama of the shape first unfolded on various; orientations to produce multiple rectified images as input of; recognition frame; the recognition system consists of a multi-branch; CNN, which analyzes the panoramas as a whole to produce the final; recognition result. The experiment results of retrieval and; classification on various of 3D dataset showed that the performance of; our method is better than the state-of-the-art methods, and the; retrieval accuracy outperforms that of multi-view method.国家自然科学基

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure