Comparison of the diagnostic accuracy of the Pluslife Mini Dock RHAM technology with Abbott ID Now and Cepheid GenXpert: A retrospective evaluation study.

Rapid and sensitive detection of pathogens is critical in interrupting the transmission chain of infectious diseases. Currently, real-time (RT-)PCR represents the gold standard for the detection of SARS-CoV-2. RNase HII-assisted amplification (RHAM) is a promising technology, enabling reliable point-of-care (PoC) testing; however, its diagnostic accuracy has not yet been investigated.

eGAC3D: enhancing depth adaptive convolution and depth estimation for monocular 3D object pose detection.

Many alternative approaches for 3D object detection using a singular camera have been studied instead of leveraging high-precision 3D LiDAR sensors incurring a prohibitive cost. Recently, we proposed a novel approach for 3D object detection by employing a ground plane model that utilizes geometric constraints named GAC3D to improve the results of the deep-based detector. GAC3D adopts an adaptive depth convolution to replace the traditional 2D convolution to deal with the divergent context of the image's feature, leading to a significant improvement in both training convergence and testing accuracy on the KITTI 3D object detection benchmark.

GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution.

Monocular 3D object detection has recently become prevalent in autonomous driving and navigation applications due to its cost-efficiency and easy-to-embed to existent vehicles. The most challenging task in monocular vision is to estimate a reliable object's location cause of the lack of depth information in RGB images. Many methods tackle this ill-posed problem by directly regressing the object's depth or take the depth map as a supplement input to enhance the model's results.