Real-time performance investigation of a downdraft gasifier fueled by cotton stalks in a batch-mode operation

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

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Optimization of time and frequency fiber-optic links exploiting bi-directional amplifiers, based on real-time performance measurement

Optical Fiber Technology ◽

10.1016/j.yofte.2021.102465 ◽

2021 ◽

Vol 62 ◽

pp. 102465

Author(s):

Karol Salwik ◽

Łukasz Śliwczyński ◽

Przemysław Krehlik ◽

Jacek Kołodziej

Keyword(s):

Performance Measurement ◽

Real Time ◽

Fiber Optic ◽

Time Performance

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A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2021.3053863 ◽

2021 ◽

pp. 1-13

Author(s):

Andres Bell ◽

Tomas Mantecon ◽

Cesar Diaz ◽

Carlos R. del-Blanco ◽

Fernando Jaureguizar ◽

...

Keyword(s):

Real Time ◽

Vehicle Detection ◽

Time Performance

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Stochastic-Depth Ambient Occlusion

Proceedings of the ACM on Computer Graphics and Interactive Techniques ◽

10.1145/3451268 ◽

2021 ◽

Vol 4 (1) ◽

pp. 1-15

Author(s):

Jop Vermeer ◽

Leonardo Scandolo ◽

Elmar Eisemann

Keyword(s):

Real Time ◽

High Performance ◽

Depth Map ◽

Visual Quality ◽

Ambient Light ◽

Missing Information ◽

Time Performance ◽

Ambient Occlusion ◽

Depth Buffer ◽

Screen Space

Ambient occlusion (AO) is a popular rendering technique that enhances depth perception and realism by darkening locations that are less exposed to ambient light (e.g., corners and creases). In real-time applications, screen-space variants, relying on the depth buffer, are used due to their high performance and good visual quality. However, these only take visible surfaces into account, resulting in inconsistencies, especially during motion. Stochastic-Depth Ambient Occlusion is a novel AO algorithm that accounts for occluded geometry by relying on a stochastic depth map, capturing multiple scene layers per pixel at random. Hereby, we efficiently gather missing information in order to improve upon the accuracy and spatial stability of conventional screen-space approximations, while maintaining real-time performance. Our approach integrates well into existing rendering pipelines and improves the robustness of many different AO techniques, including multi-view solutions.

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Real-Time Single Image Depth Perception in the Wild with Handheld Devices

Sensors ◽

10.3390/s21010015 ◽

2020 ◽

Vol 21 (1) ◽

pp. 15

Author(s):

Filippo Aleotti ◽

Giulio Zaccaroni ◽

Luca Bartolomei ◽

Matteo Poggi ◽

Fabio Tosi ◽

...

Keyword(s):

Real Time ◽

Depth Perception ◽

Depth Estimation ◽

Autonomous Driving ◽

Estimation Methods ◽

Handheld Devices ◽

Single Image ◽

Handheld Device ◽

Time Performance ◽

In The Wild

Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild.

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