“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Additive manufacturing (AM) alias 3D printing translates computer-aided design (CAD) virtual 3D models into physical objects. By digital slicing of CAD, 3D scan, or tomography data, AM builds objects layer by layer without the need for molds or machining. AM enables decentralized fabrication of customized objects on demand by exploiting digital information storage and retrieval via the Internet. The ongoing transition from rapid prototyping to rapid manufacturing prompts new challenges for mechanical engineers and materials scientists alike. Because polymers are by far the most utilized class of materials for AM, this Review focuses on polymer processing and the development of polymers and advanced polymer systems specifically for AM. AM techniques covered include vat photopolymerization (stereolithography), powder bed fusion (SLS), material and binder jetting (inkjet and aerosol 3D printing), sheet lamination (LOM), extrusion (FDM, 3D dispensing, 3D fiber deposition, and 3D plotting), and 3D bioprinting....

Polymers for 3D Printing and Customized Additive Manufacturing

BACKGROUND Hydrogels are formed through the cross-linking of hydrophilic polymer chains within an aqueous microenvironment. The gelation can be achieved through a variety of mechanisms, spanning physical entanglement of polymer chains, electrostatic interactions, and covalent chemical cross-linking. The water-rich nature of hydrogels makes them broadly applicable to many areas, including tissue engineering, drug delivery, soft electronics, and actuators. Conventional hydrogels usually possess limited mechanical strength and are prone to permanent breakage. The lack of desired dynamic cues and structural complexity within the hydrogels has further limited their functions. Broadened applications of hydrogels, however, require advanced engineering of parameters such as mechanics and spatiotemporal presentation of active or bioactive moieties, as well as manipulation of multiscale shape, structure, and architecture. ADVANCES Hydrogels with substantially improved physicochemical properties have been enabled by rational design at the molecular level and control over multiscale architecture. For example, formulations that combine permanent polymer networks with reversibly bonding chains for energy dissipation show strong toughness and stretchability. Similar strategies may also substantially enhance the bonding affinity of hydrogels at interfaces with solids by covalently anchoring the polymer networks of tough hydrogels onto solid surfaces. Shear-thinning hydrogels that feature reversible bonds impart a fluidic nature upon application of shear forces and return back to their gel states once the forces are released. Self-healing hydrogels based on nanomaterial hybridization, electrostatic interactions, and slide-ring configurations exhibit excellent abilities in spontaneously healing themselves after damages. Additionally, harnessing techniques that can dynamically and precisely configure hydrogels have resulted in flexibility to regulate their architecture, activity, and functionality. Dynamic modulations of polymer chain physics and chemistry can lead to temporal alteration of hydrogel structures in a programmed manner. Three-dimensional printing enables architectural control of hydrogels at high precision, with a potential to further integrate elements that enable change of hydrogel configurations along prescribed paths. OUTLOOK We envision the continuation of innovation in new bioorthogonal chemistries for making hydrogels, enabling their fabrication in the presence of biological species without impairing cellular or biomolecule functions. We also foresee opportunities in the further development of more sophisticated fabrication methods that allow better-controlled hydrogel architecture across multiple length scales. In addition, technologies that precisely regulate the physicochemical properties of hydrogels in spatiotemporally controlled manners are crucial in controlling their dynamics, such as degradation and dynamic presentation of biomolecules. We believe that the fabrication of hydrogels should be coupled with end applications in a feedback loop in order to achieve optimal designs through iterations. In the end, it is the combination of multiscale constituents and complementary strategies that will enable new applications of this important class of materials.

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Advances in engineering hydrogels

Light- and ink-based three-dimensional (3D) printing methods allow the rapid design and fabrication of materials without the need for expensive tooling, dies or lithographic masks. They have led to an era of manufacturing in which computers can control the fabrication of soft matter that has tunable mechanical, electrical and other functional properties. The expanding range of printable materials, coupled with the ability to programmably control their composition and architecture across various length scales, is driving innovation in myriad applications. This is illustrated by examples of biologically inspired composites, shape-morphing systems, soft sensors and robotics that only additive manufacturing can produce.

Printing soft matter in three dimensions

The computer vision and pattern recognition communities have recently witnessed a surge of feature-based methods in object recognition and image retrieval applications. These methods allow representing images as collections of “visual words” and treat them using text search approaches following the “bag of features” paradigm. In this article, we explore analogous approaches in the 3D world applied to the problem of nonrigid shape retrieval in large databases. Using multiscale diffusion heat kernels as “geometric words,” we construct compact and informative shape descriptors by means of the “bag of features” approach. We also show that considering pairs of “geometric words” (“geometric expressions”) allows creating spatially sensitive bags of features with better discriminative power. Finally, adopting metric learning approaches, we show that shapes can be efficiently represented as binary codes. Our approach achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval benchmark.

Shape google: Geometric words and expressions for invariant shape retrieval

We propose a new design of complex self-evolving structures that vary over time due to environmental interaction. In conventional 3D printing systems, materials are meant to be stable rather than active and fabricated models are designed and printed as static objects. Here, we introduce a novel approach for simulating and fabricating self-evolving structures that transform into a predetermined shape, changing property and function after fabrication. The new locally coordinated bending primitives combine into a single system, allowing for a global deformation which can stretch, fold and bend given environmental stimulus.

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Active Printed Materials for Complex Self-Evolving Deformations

Symmetry and self-similarity are the cornerstone of Nature, exhibiting themselves through the shapes of natural creations and ubiquitous laws of physics. Since many natural objects are symmetric, the absence of symmetry can often be an indication of some anomaly or abnormal behavior. Therefore, detection of asymmetries is important in numerous practical applications, including crystallography, medical imaging, and face recognition, to mention a few. Conversely, the assumption of underlying shape symmetry can facilitate solutions to many problems in shape reconstruction and analysis. Traditionally, symmetries are described as extrinsic geometric properties of the shape. While being adequate for rigid shapes, such a description is inappropriate for non-rigid ones: extrinsic symmetry can be broken as a result of shape deformations, while its intrinsic symmetry is preserved. In this paper, we present a generalization of symmetries for non-rigid shapes and a numerical framework for their analysis, addressing the problems of full and partial exact and approximate symmetry detection and classification.

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Full and Partial Symmetries of Non-rigid Shapes

Invariant shape descriptors are instrumental in numerous shape analysis tasks including deformable shape comparison, registration, classification, and retrieval. Most existing constructions model a 3D shape as a two-dimensional surface describing the shape boundary, typically represented as a triangular mesh or a point cloud. Using intrinsic properties of the surface, invariant descriptors can be designed. One such example is the recently introduced heat kernel signature, based on the Laplace-Beltrami operator of the surface. In many applications, however, a volumetric shape model is more natural and convenient. Moreover, modeling shape deformations as approximate isometries of the volume of an object, rather than its boundary, better captures natural behavior of non-rigid deformations in many cases. Here, we extend the idea of heat kernel signature to robust isometry-invariant volumetric descriptors, and show their utility in shape retrieval. The proposed approach achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval benchmark.

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Volumetric heat kernel signatures

Symmetry and self-similarity is the cornerstone of Nature, exhibiting itself through the shapes of natural creations and ubiquitous laws of physics. Since many natural objects are symmetric, the absence of symmetry can often be an indication of some anomaly or abnormal behavior. Therefore, detection of asymmetries is important in numerous practical applications, including crystallography, medical imaging, and face recognition, to mention a few. Conversely, the assumption of underlying shape symmetry can facilitate solutions to many problems in shape reconstruction and analysis. Traditionally, symmetries are described as extrinsic geometric properties of the shape. While being adequate for rigid shapes, such a description is inappropriate for non-rigid ones. Extrinsic symmetry can be broken as a result of shape deformations, while its intrinsic symmetry is preserved. In this paper, we pose the problem of finding intrinsic symmetries of non-rigid shapes and propose an efficient method for their computation.

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Symmetries of non-rigid shapes

Imaging through thick highly scattering media (sample thickness ≫ mean free path) can realize broad applications in biomedical and industrial imaging as well as remote sensing. Here we propose a computational “All Photons Imaging” (API) framework that utilizes time-resolved measurement for imaging through thick volumetric scattering by using both early arrived (non-scattered) and diffused photons. As opposed to other methods which aim to lock on specific photons (coherent, ballistic, acoustically modulated, etc.), this framework aims to use all of the optical signal. Compared to conventional early photon measurements for imaging through a 15 mm tissue phantom, our method shows a two fold improvement in spatial resolution (4db increase in Peak SNR). This all optical, calibration-free framework enables widefield imaging through thick turbid media and opens new avenues in non-invasive testing, analysis and diagnosis.

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Dan Raviv

Papers

Active Printed Materials for Complex Self-Evolving Deformations

Full and Partial Symmetries of Non-rigid Shapes

Volumetric heat kernel signatures

Symmetries of non-rigid shapes

All Photons Imaging Through Volumetric Scattering