135 research outputs found
cMinMax: A Fast Algorithm to Find the Corners of an N-dimensional Convex Polytope
During the last years, the emerging field of Augmented & Virtual Reality
(AR-VR) has seen tremendousgrowth. At the same time there is a trend to develop
low cost high-quality AR systems where computing poweris in demand. Feature
points are extensively used in these real-time frame-rate and 3D applications,
thereforeefficient high-speed feature detectors are necessary. Corners are such
special features and often are used as thefirst step in the marker alignment in
Augmented Reality (AR). Corners are also used in image registration
andrecognition, tracking, SLAM, robot path finding and 2D or 3D object
detection and retrieval. Therefore thereis a large number of corner detection
algorithms but most of them are too computationally intensive for use
inreal-time applications of any complexity. Many times the border of the image
is a convex polygon. For thisspecial, but quite common case, we have developed
a specific algorithm, cMinMax. The proposed algorithmis faster, approximately
by a factor of 5 compared to the widely used Harris Corner Detection algorithm.
Inaddition is highly parallelizable. The algorithm is suitable for the fast
registration of markers in augmentedreality systems and in applications where a
computationally efficient real time feature detector is necessary.The algorithm
can also be extended to N-dimensional polyhedrons.Comment: Accepted in GRAPP 202
Aggressive saliency-aware point cloud compression
The increasing demand for accurate representations of 3D scenes, combined
with immersive technologies has led point clouds to extensive popularity.
However, quality point clouds require a large amount of data and therefore the
need for compression methods is imperative. In this paper, we present a novel,
geometry-based, end-to-end compression scheme, that combines information on the
geometrical features of the point cloud and the user's position, achieving
remarkable results for aggressive compression schemes demanding very small bit
rates. After separating visible and non-visible points, four saliency maps are
calculated, utilizing the point cloud's geometry and distance from the user,
the visibility information, and the user's focus point. A combination of these
maps results in a final saliency map, indicating the overall significance of
each point and therefore quantizing different regions with a different number
of bits during the encoding process. The decoder reconstructs the point cloud
making use of delta coordinates and solving a sparse linear system. Evaluation
studies and comparisons with the geometry-based point cloud compression (G-PCC)
algorithm by the Moving Picture Experts Group (MPEG), carried out for a variety
of point clouds, demonstrate that the proposed method achieves significantly
better results for small bit rates
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers
In this paper we delve into the properties of transformers, attained through
self-supervision, in the point cloud domain. Specifically, we evaluate the
effectiveness of Masked Autoencoding as a pretraining scheme, and explore
Momentum Contrast as an alternative. In our study we investigate the impact of
data quantity on the learned features, and uncover similarities in the
transformer's behavior across domains. Through comprehensive visualiations, we
observe that the transformer learns to attend to semantically meaningful
regions, indicating that pretraining leads to a better understanding of the
underlying geometry. Moreover, we examine the finetuning process and its effect
on the learned representations. Based on that, we devise an unfreezing strategy
which consistently outperforms our baseline without introducing any other
modifications to the model or the training pipeline, and achieve
state-of-the-art results in the classification task among transformer models
Biometric Keys for the Encryption of Multimodal Signatures
Electricity, electromagnetism & magnetis
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