3,346 research outputs found
Registration Combining Wide and Narrow Baseline Feature Tracking Techniques for Markerless AR Systems
Augmented reality (AR) is a field of computer research which deals with the combination of real world and computer generated data. Registration is one of the most difficult problems currently limiting the usability of AR systems. In this paper, we propose a novel natural feature tracking based registration method for AR applications. The proposed method has following advantages: (1) it is simple and efficient, as no man-made markers are needed for both indoor and outdoor AR applications; moreover, it can work with arbitrary geometric shapes including planar, near planar and non planar structures which really enhance the usability of AR systems. (2) Thanks to the reduced SIFT based augmented optical flow tracker, the virtual scene can still be augmented on the specified areas even under the circumstances of occlusion and large changes in viewpoint during the entire process. (3) It is easy to use, because the adaptive classification tree based matching strategy can give us fast and accurate initialization, even when the initial camera is different from the reference image to a large degree. Experimental evaluations validate the performance of the proposed method for online pose tracking and augmentation
Discriminant Projective Non-Negative Matrix Factorization
Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W-T X as their coefficients, i.e., X approximate to WWT X. Since PNM
Chrion: Optimizing Recurrent Neural Network Inference by Collaboratively Utilizing CPUs and GPUs
Deploying deep learning models in cloud clusters provides efficient and
prompt inference services to accommodate the widespread application of deep
learning. These clusters are usually equipped with host CPUs and accelerators
with distinct responsibilities to handle serving requests, i.e. generalpurpose
CPUs for input preprocessing and domain-specific GPUs for forward computation.
Recurrent neural networks play an essential role in handling temporal inputs
and display distinctive computation characteristics because of their high
inter-operator parallelism. Hence, we propose Chrion to optimize recurrent
neural network inference by collaboratively utilizing CPUs and GPUs. We
formulate the model deployment in the CPU-GPU cluster as an NP-hard scheduling
problem of directed acyclic graphs on heterogeneous devices. Given an input
model in the ONNX format and user-defined SLO requirement, Chrion firstly
preprocesses the model by model parsing and profiling, and then partitions the
graph to select execution devices for each operator. When an online request
arrives, Chrion performs forward computation according to the graph partition
by executing the operators on the CPU and GPU in parallel. Our experimental
results show that the execution time can be reduced by 19.4% at most in the
latency-optimal pattern and GPU memory footprint by 67.5% in the memory-optimal
pattern compared with the execution on the GPU
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