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

Efficient 3D Volume Reconstruction from a Point Cloud Using a Phase-Field Method

We propose an explicit hybrid numerical method for the efficient 3D volume reconstruction from unorganized point clouds using a phase-field method. The proposed three-dimensional volume reconstruction algorithm is based on the 3D binary image segmentation method. First, we define a narrow band domain embedding the unorganized point cloud and an edge indicating function. Second, we define a good initial phase-field function which speeds up the computation significantly. Third, we use a recently developed explicit hybrid numerical method for solving the three-dimensional image segmentation model to obtain efficient volume reconstruction from point cloud data. In order to demonstrate the practical applicability of the proposed method, we perform various numerical experiments

A Mixture of Manhattan Frames: Beyond the Manhattan World

Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches to scene representation exploit this phenomenon via the somewhat restrictive assumption that every plane is perpendicular to one of the axes of a single coordinate system. Known as the Manhattan-World model, this assumption is widely used in computer vision and robotics. The complexity of many real-world scenes, however, necessitates a more flexible model. We propose a novel probabilistic model that describes the world as a mixture of Manhattan frames: each frame defines a different orthogonal coordinate system. This results in a more expressive model that still exploits the orthogonality constraints. We propose an adaptive Markov-Chain Monte-Carlo sampling algorithm with Metropolis-Hastings split/merge moves that utilizes the geometry of the unit sphere. We demonstrate the versatility of our Mixture-of-Manhattan-Frames model by describing complex scenes using depth images of indoor scenes as well as aerial-LiDAR measurements of an urban center. Additionally, we show that the model lends itself to focal-length calibration of depth cameras and to plane segmentation.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Award N00014-11-1-0688)United States. Defense Advanced Research Projects Agency (Award FA8650-11-1-7154)Technion, Israel Institute of Technology (MIT Postdoctoral Fellowship Program