91,652 research outputs found
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Superquadrics for segmentation and modeling range data
We present a novel approach to reliable and efficient recovery of part-descriptions in terms of superquadric models from range data. We show that superquadrics can directly be recovered from unsegmented data, thus avoiding any presegmentation steps (e.g., in terms of surfaces). The approach is based on the recover-andselect paradigm. We present several experiments on real and synthetic range images, where we demonstrate the stability of the results with respect to viewpoint and noise
Hierarchical macro-nanoporous metals for leakage-free high-thermal conductivity shape-stabilized phase change materials
Impregnation of Phase Change Materials (PCMs) into a porous medium is a
promising way to stabilize their shape and improve thermal conductivity which
are essential for thermal energy storage and thermal management of small-size
applications, such as electronic devices or batteries. However, in these
composites a general understanding of how leakage is related to the
characteristics of the porous material is still lacking. As a result, the
energy density and the antileakage capability are often antagonistically
coupled. In this work we overcome the current limitations, showing that a high
energy density can be reached together with superior anti-leakage performance
by using hierarchical macro-nanoporous metals for PCMs impregnation. By
analyzing capillary phenomena and synthesizing a new type of material, it was
demonstrated that a hierarchical trimodal macro-nanoporous metal (copper)
provides superior antileakage capability (due to strong capillary forces of
nanopores), high energy density (90vol% of PCM load due to macropores) and
improves the charging/discharging kinetics, due to a three-fold enhancement of
thermal conductivity. It was further demonstrated by CFD simulations that such
a composite can be used for thermal management of a battery pack and unlike
pure PCM it is capable of maintaining the maximum temperature below the safety
limit. The present results pave the way for the application of hierarchical
macro-nanoporous metals for high-energy density, leakage-free, and
shape-stabilized PCMs with enhanced thermal conductivity. These innovative
composites can significantly facilitate the thermal management of compact
systems such as electronic devices or high-power batteries by improving their
efficiency, durability and sustainabilit
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