39,671 research outputs found
Recommended from our members
Constraint-based adaptation for complex space configuration in building services
In this paper an object-based CAD programming is used to take advantage of standardization to handle the schematic design, sizing and layout planning for ceiling mounted fan coil system in a building ceiling void. In order to deal with more complex geometry and real building size, we have used a hybrid approach combining case-based reasoning and constraint programming techniques. Very often, building services engineers use previous solutions and adapt them to new problems. Case-based reasoning mirrors this practical approach and did help us deal effectively with increasingly complex geometry. Our approach combines automation and interactivity. From the specification of the building 3D BIM model, our software prototype proceeds through four steps. First, the user divides the building into zones, each zone being defined by a geometrical primitive (i.e. rectangle zone, triangle zone, curved zone, etc.). Next, for each zone a similar case is retrieved from the case library. The retrieval process will generate a first incomplete 3D solution containing some inconsistencies. Next, the incomplete solution is adapted, using constraint programming techniques, to provide a consistent solution. Finally, distribution routes (i.e. ducts and pipes) are generated using constraint programming techniques. The 3D fan coil solution can be modified or improved by the designer, while providing further contribution by concentrating on interactivity. The project has been funded by the Engineering and Physical Sciences Research Council (EPSRC) in the UK
Propagators and Violation Functions for Geometric and Workload Constraints Arising in Airspace Sectorisation
Airspace sectorisation provides a partition of a given airspace into sectors,
subject to geometric constraints and workload constraints, so that some cost
metric is minimised. We make a study of the constraints that arise in airspace
sectorisation. For each constraint, we give an analysis of what algorithms and
properties are required under systematic search and stochastic local search
Higher-order Projected Power Iterations for Scalable Multi-Matching
The matching of multiple objects (e.g. shapes or images) is a fundamental
problem in vision and graphics. In order to robustly handle ambiguities, noise
and repetitive patterns in challenging real-world settings, it is essential to
take geometric consistency between points into account. Computationally, the
multi-matching problem is difficult. It can be phrased as simultaneously
solving multiple (NP-hard) quadratic assignment problems (QAPs) that are
coupled via cycle-consistency constraints. The main limitations of existing
multi-matching methods are that they either ignore geometric consistency and
thus have limited robustness, or they are restricted to small-scale problems
due to their (relatively) high computational cost. We address these
shortcomings by introducing a Higher-order Projected Power Iteration method,
which is (i) efficient and scales to tens of thousands of points, (ii)
straightforward to implement, (iii) able to incorporate geometric consistency,
(iv) guarantees cycle-consistent multi-matchings, and (iv) comes with
theoretical convergence guarantees. Experimentally we show that our approach is
superior to existing methods
Performance of algebraic multigrid methods for non-symmetric matrices arising in particle methods
Large linear systems with sparse, non-symmetric matrices arise in the
modeling of Markov chains or in the discretization of convection-diffusion
problems. Due to their potential to solve sparse linear systems with an effort
that is linear in the number of unknowns, algebraic multigrid (AMG) methods are
of fundamental interest for such systems. For symmetric positive definite
matrices, fundamental theoretical convergence results are established, and
efficient AMG solvers have been developed. In contrast, for non-symmetric
matrices, theoretical convergence results have been provided only recently. A
property that is sufficient for convergence is that the matrix be an M-matrix.
In this paper, we present how the simulation of incompressible fluid flows with
particle methods leads to large linear systems with sparse, non-symmetric
matrices. In each time step, the Poisson equation is approximated by meshfree
finite differences. While traditional least squares approaches do not guarantee
an M-matrix structure, an approach based on linear optimization yields
optimally sparse M-matrices. For both types of discretization approaches, we
investigate the performance of a classical AMG method, as well as an AMLI type
method. While in the considered test problems, the M-matrix structure turns out
not to be necessary for the convergence of AMG, problems can occur when it is
violated. In addition, the matrices obtained by the linear optimization
approach result in fast solution times due to their optimal sparsity.Comment: 16 pages, 7 figure
Efficient Deformable Shape Correspondence via Kernel Matching
We present a method to match three dimensional shapes under non-isometric
deformations, topology changes and partiality. We formulate the problem as
matching between a set of pair-wise and point-wise descriptors, imposing a
continuity prior on the mapping, and propose a projected descent optimization
procedure inspired by difference of convex functions (DC) programming.
Surprisingly, in spite of the highly non-convex nature of the resulting
quadratic assignment problem, our method converges to a semantically meaningful
and continuous mapping in most of our experiments, and scales well. We provide
preliminary theoretical analysis and several interpretations of the method.Comment: Accepted for oral presentation at 3DV 2017, including supplementary
materia
A survey of real-time crowd rendering
In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft
- …