3,468 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
Data-driven shape analysis and processing
Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
We present DRLViz, a visual analytics interface to interpret the internal
memory of an agent (e.g. a robot) trained using deep reinforcement learning.
This memory is composed of large temporal vectors updated when the agent moves
in an environment and is not trivial to understand due to the number of
dimensions, dependencies to past vectors, spatial/temporal correlations, and
co-correlation between dimensions. It is often referred to as a black box as
only inputs (images) and outputs (actions) are intelligible for humans. Using
DRLViz, experts are assisted to interpret decisions using memory reduction
interactions, and to investigate the role of parts of the memory when errors
have been made (e.g. wrong direction). We report on DRLViz applied in the
context of video games simulators (ViZDoom) for a navigation scenario with item
gathering tasks. We also report on experts evaluation using DRLViz, and
applicability of DRLViz to other scenarios and navigation problems beyond
simulation games, as well as its contribution to black box models
interpretability and explainability in the field of visual analytics
Video browsing interfaces and applications: a review
We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other
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