Deep Learning Methods for Calibrated Photometric Stereo and Beyond.

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel.

Surface Geometry Processing: An Efficient Normal-Based Detail Representation.

With the rapid development of high-resolution 3D vision applications, the traditional way of manipulating surface detail requires considerable memory and computing time. To address these problems, we introduce an efficient surface detail processing framework in 2D normal domain, which extracts new normal feature representations as the carrier of micro geometry structures that are illustrated both theoretically and empirically in this article. Compared with the existing state of the arts, we verify and demonstrate that the proposed normal-based representation has three important properties, including detail separability, detail transferability and detail idempotence.

Efficient Computer-Aided Design of Dental Inlay Restoration: A Deep Adversarial Framework.

Restoring the normal masticatory function of broken teeth is a challenging task primarily due to the defect location and size of a patient's teeth. In recent years, although some representative image-to-image transformation methods (e.g.

Mesh-Based Computation for Solving Photometric Stereo With Near Point Lighting.

We tackle the problem of dense reconstruction with a practical system, in which near point lighting (NPL) is employed. Different from the conventional formulation of photometric stereo (PS) that assumes parallel lighting, PS under the NPL condition is a nonlinear problem as the local surface normals are coupled with its distance to the camera as well as the light sources. After obtaining the locations of point lights by a calibration process, we develop a new framework to solve this nonlinear reconstruction problem via mesh deformation, in which each facet is corresponding to a pixel in the image captured by the camera.

Highly chemoresistive humidity sensing using poly(ionic liquid)s.

A novel resistance type humidity sensor was fabricated using poly(ionic liquid)s PEVIm-Br, which not only exhibited high sensitivity at a relative humidity (RH) in the range of 11-98%, but also featured short response and recovery time, as well as small hysteresis and good repeatability, proving poly(ionic liquid)s to be promising sensing materials for high-performance humidity sensors.

Low-Temperature H2S Detection with Hierarchical Cr-Doped WO3 Microspheres.

Hierarchical Cr-doped WO3 microspheres have been successfully synthesized for efficient sensing of H2S gas at low temperatures. The hierarchical structures provide an effective gas diffusion path via well-aligned micro-, meso-, and macroporous architectures, resulting in significant enhancement in sensing response to H2S. The temperature and gas concentration dependence on the sensing properties elucidate that Cr dopants remarkably improve the response and lower the sensor' operating temperature down to 80 °C.

Temperature-Dependent Abnormal and Tunable p-n Response of Tungsten Oxide--Tin Oxide Based Gas Sensors.

We observed the sensing response of temperature-dependent abnormal p-n transitions in WO3-SnO2 hybrid hollow sphere based gas sensors for the first time. The sensors presented a normal n-type response to ethanol at elevated temperatures but abnormal p-type responses in a wide range of operation temperatures (room temperature to about 95 °C). By measuring various reducing gases and applying complex impedance plotting techniques, we demonstrated the abnormal p-type sensing behavior to be a pseudo-response resulting from the reaction between target gas and adsorbed water on the material surface.

Extracting valley-ridge lines from point-cloud-based 3D fingerprint models.

3D fingerprinting is an emerging technology with the distinct advantage of touchless operation. More important, 3D fingerprint models contain more biometric information than traditional 2D fingerprint images. However, current approaches to fingerprint feature detection usually must transform the 3D models to a 2D space through unwrapping or other methods, which might introduce distortions.

Surrounding sensitive electronic properties of Bi₂Te₃ nanoplates-potential sensing applications of topological insulators.

Significant efforts have been paid to exploring the fundamental properties of topological insulators (TIs) in recent years. However, the investigation of TIs as functional materials for practical device applications is still quite limited. In this work, electronic sensors based on Bi2Te3 nanoplates were fabricated and the sensing performance was investigated.

Strongly coupled hybrid nanostructures for selective hydrogen detection--understanding the role of noble metals in reducing cross-sensitivity.

Noble metal-semiconductor hybrid nanostructures can offer outperformance to gas sensors in terms of sensitivity and selectivity. In this work, a catalytically activated (CA) hydrogen sensor is realized based on strongly coupled Pt/Pd-WO3 hybrid nanostructures constructed by a galvanic replacement participated solvothermal procedure. The room-temperature operation and high selectivity distinguish this sensor from the traditional ones.