1,255 research outputs found
Do-It-Yourself Single Camera 3D Pointer Input Device
We present a new algorithm for single camera 3D reconstruction, or 3D input
for human-computer interfaces, based on precise tracking of an elongated
object, such as a pen, having a pattern of colored bands. To configure the
system, the user provides no more than one labelled image of a handmade
pointer, measurements of its colored bands, and the camera's pinhole projection
matrix. Other systems are of much higher cost and complexity, requiring
combinations of multiple cameras, stereocameras, and pointers with sensors and
lights. Instead of relying on information from multiple devices, we examine our
single view more closely, integrating geometric and appearance constraints to
robustly track the pointer in the presence of occlusion and distractor objects.
By probing objects of known geometry with the pointer, we demonstrate
acceptable accuracy of 3D localization.Comment: 8 pages, 6 figures, 2018 15th Conference on Computer and Robot Visio
PU-Ray: Point Cloud Upsampling via Ray Marching on Implicit Surface
While the recent advancements in deep-learning-based point cloud upsampling
methods improve the input to autonomous driving systems, they still suffer from
the uncertainty of denser point generation resulting from end-to-end learning.
For example, due to the vague training objectives of the models, their
performance depends on the point distributions of the input and the ground
truth. This causes problems of domain dependency between synthetic and
real-scanned point clouds and issues with substantial model sizes and dataset
requirements. Additionally, many existing methods upsample point clouds with a
fixed scaling rate, making them inflexible and computationally redundant. This
paper addresses the above problems by proposing a ray-based upsampling approach
with an arbitrary rate, where a depth prediction is made for each query ray.
The method simulates the ray marching algorithm to achieve more precise and
stable ray-depth predictions through implicit surface learning. The rule-based
mid-point query sampling method enables a uniform output point distribution
without requiring model training using the Chamfer distance loss function,
which can exhibit bias towards the training dataset. Self-supervised learning
becomes possible with accurate ground truths within the input point cloud. The
results demonstrate the method's versatility across different domains and
training scenarios with limited computational resources and training data. This
allows the upsampling task to transition from academic research to real-world
applications.Comment: 13 pages (10 main + 3 supplement), 19 figures (10 main + 9
supplement), 6 table
Learning to Recover Spectral Reflectance from RGB Images
This paper tackles spectral reflectance recovery (SRR) from RGB images. Since
capturing ground-truth spectral reflectance and camera spectral sensitivity are
challenging and costly, most existing approaches are trained on synthetic
images and utilize the same parameters for all unseen testing images, which are
suboptimal especially when the trained models are tested on real images because
they never exploit the internal information of the testing images. To address
this issue, we adopt a self-supervised meta-auxiliary learning (MAXL) strategy
that fine-tunes the well-trained network parameters with each testing image to
combine external with internal information. To the best of our knowledge, this
is the first work that successfully adapts the MAXL strategy to this problem.
Instead of relying on naive end-to-end training, we also propose a novel
architecture that integrates the physical relationship between the spectral
reflectance and the corresponding RGB images into the network based on our
mathematical analysis. Besides, since the spectral reflectance of a scene is
independent to its illumination while the corresponding RGB images are not, we
recover the spectral reflectance of a scene from its RGB images captured under
multiple illuminations to further reduce the unknown. Qualitative and
quantitative evaluations demonstrate the effectiveness of our proposed network
and of the MAXL. Our code and data are available at
https://github.com/Dong-Huo/SRR-MAXL
A case of severe Plasmodium knowlesi in a splenectomized patient
Plasmodium knowlesi, a zoonotic malaria, is now considered the fifth species of Plasmodium causing malaria in humans. With its 24-hour erythrocytic stage of development, it has raised concern regarding its high potential in replicating and leading to severe illness. Spleen is an important site for removal of parasitized red blood cells and generating immunity. We reported a case of knowlesi malaria in a non-immune, splenectomized patient. We observed the delay in parasite clearance, high parasitic counts, and severe illness at presentation. A thorough search through literature revealed several case reports on falciparum and vivax malaria in splenectomized patients. However, literature available for knowlesi malaria in splenectomized patient is limited. Further studies need to be carried out to clarify the role of spleen in host defense against human malaria especially P. knowlesi
Direct transfer of viral and cellular proteins from varicella-zoster virus-infected non-neuronal cells to human axons
10.1371/journal.pone.0126081PLoS ONE105e012608
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