3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral

A level-set method for the evolution of cells and tissue during curvature-controlled growth

Most biological tissues grow by the synthesis of new material close to the tissue's interface, where spatial interactions can exert strong geometric influences on the local rate of growth. These geometric influences may be mechanistic, or cell behavioural in nature. The control of geometry on tissue growth has been evidenced in many in-vivo and in-vitro experiments, including bone remodelling, wound healing, and tissue engineering scaffolds. In this paper, we propose a generalisation of a mathematical model that captures the mechanistic influence of curvature on the joint evolution of cell density and tissue shape during tissue growth. This generalisation allows us to simulate abrupt topological changes such as tissue fragmentation and tissue fusion, as well as three dimensional cases, through a level-set-based method. The level-set method developed introduces another Eulerian field than the level-set function. This additional field represents the surface density of tissue synthesising cells, anticipated at future locations of the interface. Numerical tests performed with this level-set-based method show that numerical conservation of cells is a good indicator of simulation accuracy, particularly when cusps develop in the tissue's interface. We apply this new model to several situations of curvature-controlled tissue evolutions that include fragmentation and fusion.Comment: 15 pages, 10 figures, 3 supplementary figure

Redesign optimization for manufacturing using additive layer techniques

Improvements in additive manufacturing technologies have the potential to greatly provide value to designers that could also contribute towards improving the sustainability levels of products as well as the production of lightweight products. With these improvements, it is possible to eliminate the design restrictions previously faced by manufacturers. This study examines the principles of additive manufacturing, design guidelines, capabilities of the manufacturing processes and structural optimisation using topology optimisation. Furthermore, a redesign methodology is proposed and illustrated through a redesign case study of an existing bracket. The optimal design is selected using multi-criteria decision analysis method. The challenges for using additive manufacturing technologies are discussed

Visualizing the hotspots and emerging trends of 3D printing through scientometrics

Purpose - 3D printing is believed to be driving the third industrial revolution. A comprehensive understanding of the hotspots and trends of 3D printing may promote the theory development of 3D printing, help researchers to determine the research direction, and provide a reference for enterprises and government to plan the development of 3D printing industry. However, a scientometric visualizing of 3D printing research and an exploration its hotspots and emerging trends are lacking. Therefore, it was necessary to carry out this relevant research. Design/methodology/approach – Based on the theory of scientometrics, 2769 literatures on the 3D printing theme were found in the Web of Science Core Collection’ SCI indexes between 1995-2016. These were analyzed to explore the research hotspots and emerging trends of 3D printing with the software CiteSpaceIII. Findings – (1) hotspots appeared first in 1993, grow rapidly from 2005, and peaked in 2013; (2) hotspots in the "medical field" appeared earliest and have remained extremely active; (3) hotspots have evolved from “drug”, "printer", "rapid prototyping" and "3D printing" in the 1990s, through "laser-induced consolidation", "scaffolds", "sintering" and "metal matrix composites" in the 2000s, to the current hotspots of "stereolithography", "laser additive manufacturing", "medical images", etc.; (4) "3D bioprinting",“titanium”, “stem cell” and "chemical reaction" were the emerging hotspots in recent years; (5) "commercial operation" and "fusion with emerging technology such as big data" may create future hotspots. Research limitations/implications - It is hard to avoid the possibility of missing important research results on 3D printing. The relevant records could be missing if the query phrases for topic search do not appear in records. Besides, in order to improve the quality of data, this study selected articles and reviews as the research objects, which may also omit some records. Originality/value - First, this is the first paper visualizing the hotspots and emerging trends of 3D printing using scientometric tools. Second, not only "burst reference" and "burst keywords", but also "cluster" and "landmark article" are also selected as the evaluation factors to judge the hotspots and trends of a domain comprehensively. Third, overall perspective of hotspots and trends of 3D printing is put forward for the first time

Extreme mechanical resilience of self-assembled nanolabyrinthine materials

Low-density materials with tailorable properties have attracted attention for decades, yet stiff materials that can resiliently tolerate extreme forces and deformation while being manufactured at large scales have remained a rare find. Designs inspired by nature, such as hierarchical composites and atomic lattice-mimicking architectures, have achieved optimal combinations of mechanical properties but suffer from limited mechanical tunability, limited long-term stability, and low-throughput volumes that stem from limitations in additive manufacturing techniques. Based on natural self-assembly of polymeric emulsions via spinodal decomposition, here we demonstrate a concept for the scalable fabrication of nonperiodic, shell-based ceramic materials with ultralow densities, possessing features on the order of tens of nanometers and sample volumes on the order of cubic centimeters. Guided by simulations of separation processes, we numerically show that the curvature of self-assembled shells can produce close to optimal stiffness scaling with density, and we experimentally demonstrate that a carefully chosen combination of topology, geometry, and base material results in superior mechanical resilience in the architected product. Our approach provides a pathway to harnessing self-assembly methods in the design and scalable fabrication of beyond-periodic and nonbeam-based nano-architected materials with simultaneous directional tunability, high stiffness, and unsurpassed recoverability with marginal deterioration

Functional rewiring across spinal injuries via biomimetic nanofiber scaffolds

The regrowth of severed axons is fundamental to reestablish motor control after spinal-cord injury (SCI). Ongoing efforts to promote axonal regeneration after SCI have involved multiple strategies that have been only partially successful. Our study introduces an artificial carbon-nanotube based scaffold that, once implanted in SCI rats, improves motor function recovery. Confocal microscopy analysis plus fiber tracking by magnetic resonance imaging and neurotracer labeling of long-distance corticospinal axons suggest that recovery might be partly attributable to successful crossing of the lesion site by regenerating fibers. Since manipulating SCI microenvironment properties, such as mechanical and electrical ones, may promote biological responses, we propose this artificial scaffold as a prototype to exploit the physics governing spinal regenerative plasticity

Co-regulation of learning in computer-supported collaborative learning environments: A discussion

This discussion paper for this special issue examines co-regulation of learning in computer-supported collaborative learning (CSCL) environments extending research on self-regulated learning in computerbased environments. The discussion employs a socio-cognitive perspective focusing on social and collective views of learning to examine how students co-regulate and collaborate in computer-supported inquiry. Following the review of the articles, theoretical, methodological and instructional implications are discussed: Future research directions include examining the theoretical nature of collective regulation and social metacognition in building models of co-regulated learning; expanding methodological approaches using trace data and multiple measures for convergence and construct validity; and conducting instructional experiments to test and to foster the development of co-regulated learning in computer-supported collaborative inquiry. © 2012 The Author(s).published_or_final_versio

Modulation of anabolic and catabolic responses via a porous polymer scaffold manufactured using thermally induced phase separation

We describe two studies encompassing the iterative refinement of a polymer-based rhBMP-2 delivery system for bone tissue engineering. Firstly, we compared the boneforming capacity of porous poly(D,L-lactic-co-glycolic acid) (PLGA) scaffolds produced by thermally induced phase separation (TIPS) with non-porous solvent cast poly(D,L-lactic acid) (PDLLA) used previously. Secondly, we examined the potential synergy between rhBMP-2 and local bisphosphonate in the PLGA scaffold system. In vivo ectopic bone formation studies were performed in C57BL6/J mice. Polymer scaffolds containing 0, 5, 10 or 20 μg rhBMP-2 were inserted into the dorsal musculature. At all rhBMP-2 doses, porous PLGA produced significantly higher bone volume (BV, mm) than the solid PDLLA scaffolds. Next, porous PLGA scaffolds containing 10μg rhBMP-2 ±0.2, or 2μg zoledronic acid (ZA) were inserted into the hind-limb musculature. Co-delivery of local 10μg rhBMP-2/2μg ZA significantly augmented bone formation compared with rhBMP-2 alone (400 % BV increase, p < 0.01). Hydroxyapatite microparticle (HAp) addition (2% w/w) to the 10μg rhBMP-2/0.2μg ZA group increased BV (200 %, p < 0.01). We propose that this was due to controlled ZA release of HAp-bound ZA. Consistent with this, elution analyses showed that HAp addition did not alter the rhBMP-2 elution, but delayed ZA release. Moreover, 2 % w/w HAp addition reduced the scaffold's compressive properties, but did not alter ease of surgical handling. In summary, our data show that refinement of the polymer selection and scaffold fabrication can enhance rhBMP-2 induced bone formation in our bone tissue engineering implant, and this can be further optimised by the local co-delivery of ZA/HAp