Natural exploration of 3D massive models on large-scale light field displays using the FOX proximal navigation technique

2012 ◽  
Vol 36 (8) ◽  
pp. 893-903 ◽  
Author(s):  
Fabio Marton ◽  
Marco Agus ◽  
Enrico Gobbetti ◽  
Giovanni Pintore ◽  
Marcos Balsa Rodriguez
2020 ◽  
Author(s):  
Yuan Gao

This thesis discusses approaches and techniques to convert Sparsely- Sampled Light Fields (SSLFs) into Densely-Sampled Light Fields (DSLFs), which can be used for visualization on 3DTV and Virtual Reality (VR) de- vices. Exemplarily, a movable 1D large-scale light field acquisition system for capturing SSLFs in real-world environments is evaluated. This system consists of 24 sparsely placed RGB cameras and two Kinect V2 sensors. The real-world SSLF data captured with this setup can be leveraged to reconstruct real-world DSLFs. To this end, three challenging problems require to be solved for this system: (i) how to estimate the rigid trans- formation from the coordinate system of a Kinect V2 to the coordinate system of an RGB camera; (ii) how to register the two Kinect V2 sensors with a large displacement; (iii) how to reconstruct a DSLF from a SSLF with moderate and large disparity ranges. To overcome these three challenges, we propose: (i) a novel self- calibration method, which takes advantage of the geometric constraints from the scene and the cameras, for estimating the rigid transformations from the camera coordinate frame of one Kinect V2 to the camera coordi- nate frames of 12-nearest RGB cameras; (ii) a novel coarse-to-fine approach for recovering the rigid transformation from the coordinate system of one Kinect to the coordinate system of the other by means of local color and geometry information; (iii) several novel algorithms that can be categorized into two groups for reconstructing a DSLF from an input SSLF, including novel view synthesis methods, which are inspired by the state-of-the-art video frame interpolation algorithms, and Epipolar-Plane Image (EPI) in- painting methods, which are inspired by the Shearlet Transform (ST)-based DSLF reconstruction approaches.


2021 ◽  
Vol 9 ◽  
pp. 978-994
Author(s):  
Emanuele Bugliarello ◽  
Ryan Cotterell ◽  
Naoaki Okazaki ◽  
Desmond Elliott

Abstract Large-scale pretraining and task-specific fine- tuning is now the standard methodology for many tasks in computer vision and natural language processing. Recently, a multitude of methods have been proposed for pretraining vision and language BERTs to tackle challenges at the intersection of these two key areas of AI. These models can be categorized into either single-stream or dual-stream encoders. We study the differences between these two categories, and show how they can be unified under a single theoretical framework. We then conduct controlled experiments to discern the empirical differences between five vision and language BERTs. Our experiments show that training data and hyperparameters are responsible for most of the differences between the reported results, but they also reveal that the embedding layer plays a crucial role in these massive models.


2018 ◽  
Vol 47 (6) ◽  
pp. 603004
Author(s):  
倪丽霞 Ni Lixia ◽  
李海峰 Li Haifeng ◽  
刘 旭 Liu Xu
Keyword(s):  

2021 ◽  
Author(s):  
Tingting Zhu ◽  
Lanxin Zhu ◽  
Yi Li ◽  
Xiaopeng Chen ◽  
Mingyang He ◽  
...  

We report a novel fusion of microfluidics and light-field microscopy, to achieve high-speed 4D (space + time) imaging of moving C. elegans on a chip. Our approach combines automatic chip-based worm loading / compartmentalization / flushing / reloading with instantaneous deep-learning light-field imaging of moving worm. Taken together, we realized intoto image-based screening of wild-type and uncoordinated-type worms at a volume rate of 33 Hz, with sustained observation of 1 minute per worm, and overall throughput of 42 worms per hour. With quickly yielding over 80000 image volumes that four-dimensionally visualize the dynamics of all the worms, we can quantitatively analyse their behaviours as well as the neural activities, and correlate the phenotypes with the neuron functions. The different types of worms can be readily identified as a result of the high-throughput activity mapping. Our approach shows great potential for various lab-on-a-chip biological studies, such as embryo sorting and cell growth assays.


2018 ◽  
Vol 57 (8) ◽  
pp. 1817 ◽  
Author(s):  
Lixia Ni ◽  
Zhenxing Li ◽  
Haifeng Li ◽  
Xu Liu

Author(s):  
Peter A. Kara ◽  
Maria G. Martini ◽  
Zsolt Nagy ◽  
Attila Barsi
Keyword(s):  

2018 ◽  
Vol 49 (1) ◽  
pp. 68-71
Author(s):  
Lixia Ni ◽  
Zhenxing Li ◽  
Haifeng Li ◽  
Rui Wang ◽  
Xu Liu
Keyword(s):  

2010 ◽  
Vol 26 (6-8) ◽  
pp. 1037-1047 ◽  
Author(s):  
José Antonio Iglesias Guitián ◽  
Enrico Gobbetti ◽  
Fabio Marton

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