187 research outputs found
Towards Live 3D Reconstruction from Wearable Video: An Evaluation of V-SLAM, NeRF, and Videogrammetry Techniques
Mixed reality (MR) is a key technology which promises to change the future of
warfare. An MR hybrid of physical outdoor environments and virtual military
training will enable engagements with long distance enemies, both real and
simulated. To enable this technology, a large-scale 3D model of a physical
environment must be maintained based on live sensor observations. 3D
reconstruction algorithms should utilize the low cost and pervasiveness of
video camera sensors, from both overhead and soldier-level perspectives.
Mapping speed and 3D quality can be balanced to enable live MR training in
dynamic environments. Given these requirements, we survey several 3D
reconstruction algorithms for large-scale mapping for military applications
given only live video. We measure 3D reconstruction performance from common
structure from motion, visual-SLAM, and photogrammetry techniques. This
includes the open source algorithms COLMAP, ORB-SLAM3, and NeRF using
Instant-NGP. We utilize the autonomous driving academic benchmark KITTI, which
includes both dashboard camera video and lidar produced 3D ground truth. With
the KITTI data, our primary contribution is a quantitative evaluation of 3D
reconstruction computational speed when considering live video.Comment: Accepted to 2022 Interservice/Industry Training, Simulation, and
Education Conference (I/ITSEC), 13 page
Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
The radiation dose in computed tomography (CT) examinations is harmful for
patients but can be significantly reduced by intuitively decreasing the number
of projection views. Reducing projection views usually leads to severe aliasing
artifacts in reconstructed images. Previous deep learning (DL) techniques with
sparse-view data require sparse-view/full-view CT image pairs to train the
network with supervised manners. When the number of projection view changes,
the DL network should be retrained with updated sparse-view/full-view CT image
pairs. To relieve this limitation, we present a fully unsupervised score-based
generative model in sinogram domain for sparse-view CT reconstruction.
Specifically, we first train a score-based generative model on full-view
sinogram data and use multi-channel strategy to form highdimensional tensor as
the network input to capture their prior distribution. Then, at the inference
stage, the stochastic differential equation (SDE) solver and data-consistency
step were performed iteratively to achieve fullview projection. Filtered
back-projection (FBP) algorithm was used to achieve the final image
reconstruction. Qualitative and quantitative studies were implemented to
evaluate the presented method with several CT data. Experimental results
demonstrated that our method achieved comparable or better performance than the
supervised learning counterparts.Comment: 11 pages, 12 figure
Artificial Intelligence in Civil Infrastructure Health Monitoring—historical Perspectives, Current Trends, and Future Visions
Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
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