3 research outputs found
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
SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds
This paper presents the methods that have participated in the SHREC 2022
track on the fitting and recognition of simple geometric primitives on point
clouds. As simple primitives we mean the classical surface primitives derived
from constructive solid geometry, i.e., planes, spheres, cylinders, cones and
tori. The aim of the track is to evaluate the quality of automatic algorithms
for fitting and recognising geometric primitives on point clouds. Specifically,
the goal is to identify, for each point cloud, its primitive type and some
geometric descriptors. For this purpose, we created a synthetic dataset,
divided into a training set and a test set, containing segments perturbed with
different kinds of point cloud artifacts. Among the six participants to this
track, two are based on direct methods, while four are either fully based on
deep learning or combine direct and neural approaches. The performance of the
methods is evaluated using various classification and approximation measures
SHREC 2021 Track:Retrieval and classification of protein surfaces equipped with physical and chemical properties
This paper presents the methods that have participated in the SHREC 2021
contest on retrieval and classification of protein surfaces on the basis of
their geometry and physicochemical properties. The goal of the contest is to
assess the capability of different computational approaches to identify
different conformations of the same protein, or the presence of common
sub-parts, starting from a set of molecular surfaces. We addressed two
problems: defining the similarity solely based on the surface geometry or with
the inclusion of physicochemical information, such as electrostatic potential,
amino acid hydrophobicity, and the presence of hydrogen bond donors and
acceptors. Retrieval and classification performances, with respect to the
single protein or the existence of common sub-sequences, are analysed according
to a number of information retrieval indicators