In this paper, an active method of 3D reconstruction is performed by structured light scanning using a hardware setup consisting of two cameras and a projector and is implemented using OpenCV and Python.
We propose an inno-vative MRI reconstruction framework that employs struc-tured state space models at its core, aimed at amplifying both long-range contextual sensitivity and reconstruction efficacy.
Filtering by: "Reconstructionviaastructuredresultantmatrix" CVPR2012 Li Wang Zhang Kim Lee Remove constraint "Reconstructionviaastructuredresultantmatrix" CVPR2012 Li Wang Zhang Kim Lee Language en Resource type Dissertation
This paper presents a robust 3D shape reconstruction technique that integrates structured ‐ light 3D imaging scheme with deep convolutional neural network (CNN) learning.
hod is proposed to calculate the poses of the camera relative to the world coordinate system at each shooting position. This algorithm effectively reduces the error accumulation and pose drift...
In this paper, a detailed comparison on the three types of 3D reconstruction techniques are reviewed in term of input data structure, correspondence accuracy, precision and recall using four benchmark datasets, i.e. ModelNet10/40, ICL-NUIM, and Semantic3D.
With advancements in deep neural networks and substantial training data, this field has seen dramatic developments and diverse applications, ranging from augmented reality to autonomous robotics. Traditional research predominantly focuses on passive recognition,...
Visual understanding systems have achieved remarkable success across various computer vision tasks, yet their deployment in real-world scenarios reveals a critical limitation: the inability to evolve and adapt to the dynamic nature of visual concepts.
Description: Modern deep learning and data mining techniques have demonstrated their effectiveness to comprehend and help solve real-life problems associated with various research fields dealing with different types of data which includes speech recognition, computer vision, and text processing.
A core problem in many computer vision applications is visualrecognition (including object classification, detection and localization). Recent advances in artificial neural networks (aka ”deep learning”) have significantly pushed forward the state-of-the-art visualrecognition performances.
