Geometric Deep Learning for Shape Correspondence in Mass Customization

Many industries, such as human-centric product manufacturers, are calling for mass customization with personalized products. One key enabler of mass customization is 3D printing, which makes the flexible design and manufacturing possible. However, personalized designs bring obstacles for the shape matching and analysis, owing to the high complexity and large shape variations. Traditional shape matching methods are limited to shape alignment, which cannot determine the intrinsic in-variance of mass customized models. To extract the deformations widely seen in mass customization paradigm and address the issues of alignment methods in shape matching, we redefine the geometry matching problem as a correspondence problem, and solve for the correspondence of all vertices on a queried shape to a reference shape. A state-of-the-art geometric deep learning method is used to learn the correspondence of a set of collected models. Through learning the intrinsic deformations of the products, the underlying variations of the shapes are extracted. We demonstrate the application of the proposed approach in orthodontics industry, and the experimental results show the effectiveness of the proposed method and the defined problem is favorably suitable for shape analysis in mass customization.

RADIOMETRIC CROSS-CALIBRATION OF GAOFEN-1 WFV CAMERAS WITH LANDSAT-8 OLI AND MODIS SENSORS BASED ON RADIATION AND GEOMETRY MATCHING

Cross-calibration has the advantages of high precision, low resource requirements and simple implementation. It has been widely used in recent years. The four wide-field-of-view (WFV) cameras on-board Gaofen-1 satellite provide high spatial resolution and wide combined coverage (4 × 200 km) without onboard calibration. In this paper, the four-band radiometric cross-calibration coefficients of WFV1 camera were obtained based on radiation and geometry matching taking Landsat 8 OLI (Operational Land Imager) sensor as reference. Scale Invariant Feature Transform (SIFT) feature detection method and distance and included angle weighting method were introduced to correct misregistration of WFV-OLI image pair. The radiative transfer model was used to eliminate difference between OLI sensor and WFV1 camera through the spectral match factor (SMF). The near-infrared band of WFV1 camera encompasses water vapor absorption bands, thus a Look Up Table (LUT) for SMF varies from water vapor amount is established to estimate the water vapor effects. The surface synchronization experiment was designed to verify the reliability of the cross-calibration coefficients, which seem to perform better than the official coefficients claimed by the China Centre for Resources Satellite Data and Application (CCRSDA).

Design of a High Performance Polymer Thermoelectric Generator Using Radial Architecture

Thermoelectric generators (TEGs) are solid-state heat engines consisting of p-type and n-type semiconductors that convert heat into electricity via the Seebeck effect. Conducting polymers are a viable alternative with intrinsic advantages over their inorganic counterparts since they are abundant, flexible as thick-films, and have reduced manufacturing costs since they can be solution processed. Furthermore, polymers have an inherently low thermal conductivity, thus affording them the option of forgoing some heat exchanger costs. Current examples of polymer TE devices have been limited to traditional flat-plate geometries with power densities on the μW/cm2 scale, where their potential is not fully realized. Herein, we report a novel radial device and evaluate the improved performance of polymer-based TEG based on this architecture. Analytical heat transfer and electrical models are presented that optimize the device for maximum power density, and we obtain the geometry matching condition for this radial device that maximizes the module figure-of-merit. Our radial architecture accommodates a fluid as the heat source and can utilize natural convection alone (due to heat spreading) to obtain high power densities of 1–3 mW/cm2 using state-of-the-art polymer TEs subjected to a temperature difference of 100 K.