A novel neural network called the isomorphic mesh generator (iMG) is proposed to generate isomorphic meshes from point clouds containing noise and missing parts. Isomorphic meshes of arbitrary objects exhibit a unified mesh structure, despite objects belonging to different classes. This unified representation enables various modern deep neural networks (DNNs) to easily handle surface models without requiring additional pre-processing. Additionally, the unified mesh structure of isomorphic meshes enables the application of the same process to all isomorphic meshes, unlike general mesh models, where processes need to be tailored depending on their mesh structures. Therefore, the use of isomorphic meshes can ensure efficient memory usage and reduce calculation time. Apart from the point cloud of the target object used as input for the iMG, point clouds and mesh models need not be prepared in advance as training data because the iMG is a data-free method. Furthermore, the iMG outputs an isomorphic mesh obtained by mapping a reference mesh to a given input point cloud. To stably estimate the mapping function, a step-by-step mapping strategy is introduced. This strategy enables flexible deformation while simultaneously maintaining the structure of the reference mesh. Simulations and experiments conducted using a mobile phone have confirmed that the iMG reliably generates isomorphic meshes of given objects, even when the input point cloud includes noise and missing parts.

This paper presents a new system for predicting the spatial change of Nasopharyngeal carcinoma(NPC) and organ-at-risks (OARs) volumes over the course of the radiation therapy (RT) treatment for facilitating the workflow of adaptive radiotherapy. The proposed system, called “Tumor Evolution Prediction (TEP-Net)”, predicts the spatial distributions of NPC and 5 OARs, separately, in response to RT in the coming week, week n. Here, TEP-Net has (n-1)-inputs that are week 1 to week n-1 of CT axial, coronal or sagittal images acquired once the patient complete the planned RT treatment of the corresponding week. As a result, three predicted results of each target region are obtained from the three-view CT images. To determine the final prediction of NPC and 5 OARs, two integration methods, weighted fully connected layers and weighted voting methods, are introduced. From the experiments using weekly CT images of 140 NPC patients, our proposed system achieves the best performance for predicting NPC and OARs compared with conventional methods.

This paper proposes a new method for mapping volume models of human organs onto a target volume with simple shapes. The proposed method is based on our modified Self-organizing Deformable Model (mSDM) which finds the one-to-one mapping with no foldovers between an arbitrary object surface model and a target surface. By extending mSDM to apply to organ volume models, the proposed method, called volumetric SDM (vSDM), establishes the one-to-one correspondence between the volume model and its target volume. At the same time, vSDM preserves geometrical properties of the original model before and after the mapping. In addition, vSDM allows to control the mapping of interior structures of the organ model onto specific regions inside the target volume. These characteristics of vSDM enables to easily find a reliable correspondence between different volume models via a common target volume.

We propose an evaluation method of grasp stability which takes into account the elastic deformation of fingertips from the viewpoint of energy. An evaluation value of grasp stability is derived as the minimum energy which causes slippage of a fingertip on its contact surface. To formulate the evaluation value, the elastic potential energy of fingertips and the gravitational potential energy of a grasped object are considered. It is ensured that fingertips do not slip on grasped object surfaces if the external energy applied to the object is less than the evaluation value. Since our evaluation value explicitly considers the deformation values of fingertips, grasp stability is evaluated by taking into consideration the contact forces generated by the deformation. The effectiveness of our method is verified through numerical examples.

This paper presents a grasp planner which allows a robot to grasp the constricted parts of objects in our daily life. Even though constricted parts can be grasped more firmly than convex parts, previous planners have not sufficiently focused on grasping this part. We develop techniques for quadric surface approximation, grasp posture generation, and stability evaluation for grasping constricted parts. By modeling an object into multiple quadric surfaces, the planner generates a grasping posture by selecting one-sheet hyperbolic surfaces or two adjacent ellipsoids as constricted parts. When a grasping posture being generated, the grasp stability is evaluated based on the distribution of the stress applied to an object by the fingers. We perform several simulations and experiments to verify the effectiveness of our proposed method.

This paper presents a parallel algorithm of the Level Set Method named the Parallel Fast Level Set Method, and its application for real-time 3D reconstruction of human shape and motion. The Fast Level Set Method is an efficient implementation algorithm of the Level Set Method and has been applied to several applications such as object tracking in video images and 3D shape reconstruction using multiple stereo cameras. In this paper, we implement the Fast Level Set Method on a PC cluster and develop a real-time motion capture system for arbitrary viewpoint image synthesis. To obtain high performance on a PC cluster, efficient load-balancing and resource allocation algorithms are crucial problems. We develop a novel optimization technique of load distribution based on the estimation of moving direction of object boundaries. In this technique, the boundary motion is estimated in the framework of the Fast Level Set Method, and the optimum load distribution is predicted and performed according to the estimated boundary motion and the current load balance. Experiments of human shape reconstruction and arbitrary viewpoint image synthesis using the proposed system are successfully carried out.

In 3D computer graphics, mesh parameterization is a key technique for digital geometry processings(DGP) such as morphing, shape blending, texture transfer, re-meshing and so on. This paper proposes a novel approach for parameterizing a mesh into another one directly. The main idea of our method is to combine a competitive learning and a leastsquare mesh techniques. It is enough to give some semantic feature correspondences between target meshes, even if they are in different shapes or in different poses. We show the effectiveness of our approach by giving some examples of its applications.

This paper proposes a new method for integrating range images with different resolutions to generate a whole surface model of a 3D object. Generally, a modeling method using range images needs an overlapping area between the images for the registration process. Accordingly, there are redundant data in the overlapping area. In order to produce a seamless and nonredundant model, we integrate the data, and represent the overlapping area with triangular patches. However, few conventional methods have paid attention to integrating images with different resolutions. We propose a new integration method in which geometric and topological information are used. Experimental results show that our proposed method is efficient for integrating multiple range images

The paper presents a method of creating a complete model of a curved object from a sequence of range images acquired by a fixed range finder. To accomplish the modeling fast and accurately in an optimal manner, we propose a new online viewpoint planning algorithm to choose the next best viewpoint (NBV) based on the already obtained partial model. The NBV is determined by evaluating factors such as possibility of merging new data, local shape changes, registration accuracy and control point distribution.

To construct a 3D model of an object in a computer, a range finder is used to obtain range images of the object. The range finder takes range images from many view points to eliminate the influence of occlusions. A registration algorithm is, then, used to merge the range images. When the range finder is mounted on a mobile robot and measures while moving, range images taken by it will be distorted. We extended the registration algorithm to remove the deformation. The extended algorithm can remove it and is used to construct a 3D model even for a case where the speed parameters of the robot are not well known.