3D printed objects are rapidly becoming prevalent in science, technology and daily life. An important question is how to obtain strong and durable 3D models using standard printing techniques. This question is often translated to computing smartly designed interior structures that provide strong support and yield resistant 3D models. In this paper we suggest a combination between 3D printing and material injection to achieve strong 3D printed objects. We utilize triply periodic minimal surfaces (TPMS) to define novel interior support structures. TPMS are closed form and can be computed in a simple and straightforward manner. Since TPMS are smooth and connected, we utilize them to define channels that adequately distribute injected materials in the shape interior. To account for weak regions, TPMS channels are locally optimized according to the shape stress field. After the object is printed, we simply inject the TPMS channels with materials that solidify and yield a strong inner structure that supports the shape. Our method allows injecting a wide range of materials in an object interior in a fast and easy manner. Results demonstrate the efficiency of strong printing by combining 3D printing and injection together.

In this paper, we develop a local search algorithm to solve the Maximum Weight Clique (MWC) problem. Firstly we design a novel scoring function to measure the benefits of a local move. Then we develop a Cycle Estimation based ReStart (CERS) strategy to resolve the cycling issue in the local search process. Experimental results show that our solver achieves state-of-the-art performances on the large sparse graphs as well as large dense graphs. Also we present a theorem which shows the necessity of the restart strategies in current state-of-the-art local search algorithms.

In this paper, a learning-based shape descriptor for shape matching is demonstrated. Formulated in a bag-of-words like framework, the proposed method summarizes the local features extracted from certain shape to generate a integrated representation. It contributes to the speed-up of shape matching, since the distance metric in the vector space analysis can be directly applied to compare the constructed global descriptors, eliminating the time consuming stage of local feature matching. Similar to the philosophy in spatial pyramid matching, a strategy for feature division is applied in the phase of encoded feature pooling and vocabulary learning, which helps to construct a more discriminative descriptor incorporating both global and local information. Also, a local contour-based feature extraction method is designed for 2D shapes, while significant properties of the local contours are inspected for the design of feature division rules. The designed local feature extraction method and the feature division rules manage to reduce the variances of shape representation due to the changes in rotation. In addition to 2D shape, we also present a simple and natural method to extend the proposed method to the scenario of 3D shape representation. The proposed shape descriptor is validated on several benchmark data sets for evaluating 2D and 3D shape matching algorithms, and it is observed that the investigated shape descriptor maintains superior discriminative power as well as high time efficiency.