Design of Adaptive Porous Micro-structures for Additive Manufacturing

AbstractThe emerging field of additive manufacturing with biocompatible materials has led to customized design of porous micro- structures. Complex micro-structures are characterized by freeform surfaces and spatially varying porosity. Today, there is no CAD system that can handle the design of these microstructures due to their high complexity. In this paper we propose a novel approach for generating a 3D adaptive model of a porous micro-structure based on predefined design. Using our approach a designer can manually select a region of interest (ROI) and define its porosity. In the core of our approach, the multi-resolution volumetric model is used. The generation of an adaptive model may contain topological changes that should be considered. In our approach, the process of designing a customized model is composed of the following stages (a) constructing a multi- resolution volumetric model of a porous structure (b) defining regions of interest (ROI) and their resolution properties (c) constructing the adaptive model. In this research, the approach was initially tested on 2D models and then extended to 3D models. The resulted adapted model can be used for design, mechanical analysis and manufacturing. The feasibility of the method has been applied on bone models that were reconstructed from micro-CT images

Preschoolers’ views on integration of digital technologies

The aim of the present study was to explore preschool children’s views on the integration of digital technologies in their school. The study included 171 Israeli children aged 3 to 6 who participated in in-depth interviews regarding their views on digital technologies in their preschool. The interviews were analyzed using content analysis. Three major views regarding digital technologies in the preschool were found: The degree to which digital technologies are necessary; the goals of the use of these technologies; the setting for using the digital technologies. Fifty percent of the children, especially the younger ones, claimed that use of these technologies is not necessary in preschool. However, most of them understood the importance of using these technologies and their contribution to many fields. In relation to the setting use, they referred to time and social aspects. The findings indicate that preschool teachers need to mediate these aspects more wisely and adapt them to the children's understanding and view toward digital technologies than actually takes place when they use them with the children

CloudWalker: Random walks for 3D point cloud shape analysis

Point clouds are gaining prominence as a method for representing 3D shapes, but their irregular structure poses a challenge for deep learning methods. In this paper we propose CloudWalker, a novel method for learning 3D shapes using random walks. Previous works attempt to adapt Convolutional Neural Networks (CNNs) or impose a grid or mesh structure to 3D point clouds. This work presents a different approach for representing and learning the shape from a given point set. The key idea is to impose structure on the point set by multiple random walks through the cloud for exploring different regions of the 3D object. Then we learn a per-point and per-walk representation and aggregate multiple walk predictions at inference. Our approach achieves state-of-the-art results for two 3D shape analysis tasks: classification and retrieval

DiGS : Divergence guided shape implicit neural representation for unoriented point clouds

Neural shape representations have recently shown to be effective in shape analysis and reconstruction tasks. Existing neural network methods require point coordinates and corresponding normal vectors to learn the implicit level sets of the shape. Normal vectors are often not provided as raw data, therefore, approximation and reorientation are required as pre-processing stages, both of which can introduce noise. In this paper, we propose a divergence guided shape representation learning approach that does not require normal vectors as input. We show that incorporating a soft constraint on the divergence of the distance function favours smooth solutions that reliably orients gradients to match the unknown normal at each point, in some cases even better than approaches that use ground truth normal vectors directly. Additionally, we introduce a novel geometric initialization method for sinusoidal shape representation networks that further improves convergence to the desired solution. We evaluate the effectiveness of our approach on the task of surface reconstruction and show state-of-the-art performance compared to other unoriented methods and on-par performance compared to oriented methods

PatchContrast: Self-Supervised Pre-training for 3D Object Detection

Accurately detecting objects in the environment is a key challenge for autonomous vehicles. However, obtaining annotated data for detection is expensive and time-consuming. We introduce PatchContrast, a novel self-supervised point cloud pre-training framework for 3D object detection. We propose to utilize two levels of abstraction to learn discriminative representation from unlabeled data: proposal-level and patch-level. The proposal-level aims at localizing objects in relation to their surroundings, whereas the patch-level adds information about the internal connections between the object's components, hence distinguishing between different objects based on their individual components. We demonstrate how these levels can be integrated into self-supervised pre-training for various backbones to enhance the downstream 3D detection task. We show that our method outperforms existing state-of-the-art models on three commonly-used 3D detection datasets