757 research outputs found
Lazy visibility evaluation for exact soft shadows
International audienceThis report presents a novel approach to compute high quality and alias-free soft shadows using exact visibility computations. This work relies on a theoritical framework allowing to group lines according to the geometry they intersect. From this study, we derive a new algorithm encoding lazily the visibility from a polygon. Contrary to previous works on from-polygon visibility, our approach is very robust and straightforward to implement. We apply this algorithm to solve exactly and efficiently the visibility of an area light source from any point in a scene. As a consequence, results are not sensitive to noise, contrary to soft shadows methods based on area light source sampling. We demonstrate the reliability of our approach on different scenes and configurations
Lazy visibility evaluation for exact soft shadows
Présentation invitée de l'article du même nom publié en 2012 dans la revue Computer Graphics Forum.International audienceThis paper presents a novel approach to compute high quality and noise-free soft shadows using exact visibility computations. This work relies on a theoretical framework allowing to group lines according to the geometry they intersect. From this study, we derive a new algorithm encoding lazily the visibility from a polygon. Contrary to previous works on from-polygon visibility, our approach is very robust and straightforward to implement. We apply this algorithm to solve exactly and efficiently the visibility of an area light source from any point in a scene. As a consequence, results are not sensitive to noise, contrary to soft shadows methods based on area light source sampling. We demonstrate the reliability of our approach on different scenes and configurations
Optimisation of parallel KD-trees using heuristics for neuron touch detection task. .
In neuroscience, the simulation of the brain and more specifically of neural circuits has allowed progress in the field. To carry out a simulation, it is first necessary to obtain the morphological representation of the neurons to be represented. They are then distributed in space and the points at which a synapse can be generated are detected. Finally, the exchange of signals is simulated. In this project, different heuristics will be tested to try to speed up the process of finding possible synapse points using parallel k-d trees.<br /
DCSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts
We present DCSG, a neural model composed of two dual and complementary
network branches, with dropouts, for unsupervised learning of compact
constructive solid geometry (CSG) representations of 3D CAD shapes. Our network
is trained to reconstruct a 3D shape by a fixed-order assembly of quadric
primitives, with both branches producing a union of primitive intersections or
inverses. A key difference between DCSG and all prior neural CSG models is
its dedicated residual branch to assemble the potentially complex shape
complement, which is subtracted from an overall shape modeled by the cover
branch. With the shape complements, our network is provably general, while the
weight dropout further improves compactness of the CSG tree by removing
redundant primitives. We demonstrate both quantitatively and qualitatively that
DCSG produces compact CSG reconstructions with superior quality and more
natural primitives than all existing alternatives, especially over complex and
high-genus CAD shapes.Comment: 9 page
Computational Geometry Column 42
A compendium of thirty previously published open problems in computational
geometry is presented.Comment: 7 pages; 72 reference
GraphStep: A System Architecture for Sparse-Graph Algorithms
Many important applications are organized around
long-lived, irregular sparse graphs (e.g., data and knowledge
bases, CAD optimization, numerical problems, simulations). The
graph structures are large, and the applications need regular
access to a large, data-dependent portion of the graph for each
operation (e.g., the algorithm may need to walk the graph, visiting
all nodes, or propagate changes through many nodes in the
graph). On conventional microprocessors, the graph structures
exceed on-chip cache capacities, making main-memory bandwidth
and latency the key performance limiters. To avoid this
“memory wall,” we introduce a concurrent system architecture
for sparse graph algorithms that places graph nodes in small
distributed memories paired with specialized graph processing
nodes interconnected by a lightweight network. This gives us a
scalable way to map these applications so that they can exploit
the high-bandwidth and low-latency capabilities of embedded
memories (e.g., FPGA Block RAMs). On typical spreading activation
queries on the ConceptNet Knowledge Base, a sample
application, this translates into an order of magnitude speedup
per FPGA compared to a state-of-the-art Pentium processor
A Survey on Actionable Knowledge
Actionable Knowledge Discovery (AKD) is a crucial aspect of data mining that
is gaining popularity and being applied in a wide range of domains. This is
because AKD can extract valuable insights and information, also known as
knowledge, from large datasets. The goal of this paper is to examine different
research studies that focus on various domains and have different objectives.
The paper will review and discuss the methods used in these studies in detail.
AKD is a process of identifying and extracting actionable insights from data,
which can be used to make informed decisions and improve business outcomes. It
is a powerful tool for uncovering patterns and trends in data that can be used
for various applications such as customer relationship management, marketing,
and fraud detection. The research studies reviewed in this paper will explore
different techniques and approaches for AKD in different domains, such as
healthcare, finance, and telecommunications. The paper will provide a thorough
analysis of the current state of AKD in the field and will review the main
methods used by various research studies. Additionally, the paper will evaluate
the advantages and disadvantages of each method and will discuss any novel or
new solutions presented in the field. Overall, this paper aims to provide a
comprehensive overview of the methods and techniques used in AKD and the impact
they have on different domains
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