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• This paper studies the problem of structured 3D reconstruction using wireframes that consist of line segments and junctions, focusing on the computation of structured boundary geometries of scenes. Instead of leveraging matching-based solutions from 2D wireframes (or line segments) for 3D wireframe reconstruction as done in prior arts, we present NEAT, a rendering-distilling formulation using neural fields to represent 3D line segments with 2D observations, and bipartite matching for perceiving and dis- tilling of a sparse set of 3D global junctions. The proposed NEAT enjoys the joint optimization of the neural fields and the global junctions from scratch, using view-dependent 2D observations without precomputed cross-view feature matching. Comprehensive experiments on the DTU and BlendedMVS datasets demonstrate our NEAT’s superiority over state-of-the-art alternatives for 3D wireframe reconstruction. Moreover, the distilled 3D global junctions by NEAT, are a better initialization than SfM points, for the recently-emerged 3D Gaussian Splatting for high-fidelity novel view synthesis using about 20 times fewer initial 3D points. 

This study presents a wearable plant tattoo sensor array designed for continuous monitoring of leaf temperature, relative water content, and biopotential. Current plant wearable sensor technologies often require relatively bulky substrates for sensor support and adhesives for leaf attachment, which potentially can hinder plant growth and affect long‐term measurements. The multifunctional tattoo sensor array overcomes these issues by adhering directly to the leaf surface without the need for additional supporting structures or glues. This array includes a biopotential electrode, a resistive temperature sensor, and an impedimetric water content sensor, all constructed using laminated gold‐on‐polymer thin‐film patterns. Due to their mechanical flexibility, stretchability, and conformability, the sensors can seamlessly attach to leaves via van der Waals force. Performances of these sensors are evaluated to explore plant responses under diverse growth environments. This sensor array is capable of both short‐term and long‐term monitoring, offering continuous data and detailed insights into plant physiological responses to various stress conditions.

 

A plasmon-enhanced pyroelectric membrane was applied to control the current flow in a graphene transistor for light detection. The graphene transistor was built on a free-standing, 15-μm-thick PVDF membrane, which was doped using gold nanorods to facilitate its optical absorption. Under the resonant condition, the device exhibited a responsivity of 0.79 μA/mW.

 

Herein, a wavelength‐selective pyroelectric sensor based on a graphene field‐effect transistor (gFET) with a plasmon‐enhanced pyroelectric gate (PG) is reported. The PG gFET (PG‐gFET) uses a poly(vinylidene fluoride‐co‐trifluoroethylene) or PVDF‐TrFE membrane doped with plasmonic nanoparticles as the gate. Gold nanorods (AuNRs) or silver nanoparticles (AgNPs) are incorporated into the PVDF‐TrFE membrane to enhance the photothermal conversion efficiency of PVDF‐TrFE in a specific narrowband wavelength range. The wavelength‐selective photothermal effect can optically modulate the gate potential of the PG‐gFET; this, in turn, leads to a change in current through the graphene film of the transistor. The PG‐gFET with AuNRs exhibits a maximum responsivity of 0.79 μA mW⁻¹at the wavelength of 660 nm. Replacing AuNRs with AgNPs in the PVDF‐TrFE membrane results in tuning the plasmonic response of the transistor to 488 nm with a maximum responsivity of 0.68 μA mW⁻¹. When plasmonic nanoparticles are absent from the PVDF‐TrFE membrane, the maximum response wavelength of the transistor is shifted to a midinfrared regime at 3125 nm, which is associated with the CC absorption of PVDF‐TrFE. The ability of the PG‐gFET to selectively respond to different light wavelengths will benefit many fields, including pyroelectric sensors, spectroscopy, and imaging.