7,187 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
The Iray Light Transport Simulation and Rendering System
While ray tracing has become increasingly common and path tracing is well
understood by now, a major challenge lies in crafting an easy-to-use and
efficient system implementing these technologies. Following a purely
physically-based paradigm while still allowing for artistic workflows, the Iray
light transport simulation and rendering system allows for rendering complex
scenes by the push of a button and thus makes accurate light transport
simulation widely available. In this document we discuss the challenges and
implementation choices that follow from our primary design decisions,
demonstrating that such a rendering system can be made a practical, scalable,
and efficient real-world application that has been adopted by various companies
across many fields and is in use by many industry professionals today
Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes
We present the Semantic Robot Programming (SRP) paradigm as a convergence of
robot programming by demonstration and semantic mapping. In SRP, a user can
directly program a robot manipulator by demonstrating a snapshot of their
intended goal scene in workspace. The robot then parses this goal as a scene
graph comprised of object poses and inter-object relations, assuming known
object geometries. Task and motion planning is then used to realize the user's
goal from an arbitrary initial scene configuration. Even when faced with
different initial scene configurations, SRP enables the robot to seamlessly
adapt to reach the user's demonstrated goal. For scene perception, we propose
the Discriminatively-Informed Generative Estimation of Scenes and Transforms
(DIGEST) method to infer the initial and goal states of the world from RGBD
images. The efficacy of SRP with DIGEST perception is demonstrated for the task
of tray-setting with a Michigan Progress Fetch robot. Scene perception and task
execution are evaluated with a public household occlusion dataset and our
cluttered scene dataset.Comment: published in ICRA 201
- …