Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking the means to exploit context information for finding correspondence in illposed regions. To tackle this problem, we propose PSM-Net, a pyramid stereo matching network consisting of two main modules: spatial pyramid pooling and 3D CNN. The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume. The 3D CNN learns to regularize cost volume using stacked multiple hourglass networks in conjunction with intermediate supervision. The proposed approach was evaluated on several benchmark datasets. Our method ranked first in the KITTI 2012 and 2015 leaderboards before March 18, 2018. The codes of PSMNet are available at: https: //github.com/JiaRenChang/PSMNet.
The design and formation of a linear assembly of gold nanorods using a biomolecular recognition system are described. Anti-mouse IgG was immobilized on the {111} end faces of gold nanorods through a thioctic acid containing a terminal carboxyl group. The biofunctionalized nanorods can be assembled with the desired length using mouse IgG for biorecognition and binding. The gold nanorods can be assembled to extended nanorod chains, which can be as long as 3 microm. These assembled nanostructures may be used as the precursors for future nanodevices.
In this review, we discuss recent advances of I–III–VI QDs with a major focus on synthesis and biomedical applications; advantages include low toxicity and fluorescent tuning in the biological window.
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