1,613 research outputs found
The Binary Space Partitioning-Tree Process
The Mondrian process represents an elegant and powerful approach for space
partition modelling. However, as it restricts the partitions to be
axis-aligned, its modelling flexibility is limited. In this work, we propose a
self-consistent Binary Space Partitioning (BSP)-Tree process to generalize the
Mondrian process. The BSP-Tree process is an almost surely right continuous
Markov jump process that allows uniformly distributed oblique cuts in a
two-dimensional convex polygon. The BSP-Tree process can also be extended using
a non-uniform probability measure to generate direction differentiated cuts.
The process is also self-consistent, maintaining distributional invariance
under a restricted subdomain. We use Conditional-Sequential Monte Carlo for
inference using the tree structure as the high-dimensional variable. The
BSP-Tree process's performance on synthetic data partitioning and relational
modelling demonstrates clear inferential improvements over the standard
Mondrian process and other related methods
Conservative From-Point Visibility.
Visibility determination has been an important part of the computer graphics
research for several decades. First studies of the visibility were hidden line
removal algorithms, and later hidden surface removal algorithms. Today’s
visibility determination is mainly concentrated on conservative, object level
visibility determination techniques. Conservative methods are used to
accelerate the rendering process when some exact visibility determination
algorithm is present. The Z-buffer is a typical exact visibility determination
algorithm. The Z-buffer algorithm is implemented in practically every modern
graphics chip.
This thesis concentrates on a subset of conservative visibility
determination techniques. These techniques are sometimes called from-point
visibility algorithms. They attempt to estimate a set of visible objects as seen
from the current viewpoint. These techniques are typically used with real-time
graphics applications such as games and virtual environments. Concentration is
on the view frustum culling and occlusion culling. View frustum culling
discards objects that are outside of the viewable volume. Occlusion culling
algorithms try to identify objects that are not visible because they are behind
some other objects. Also spatial data structures behind the efficient
implementations of view frustum culling and occlusion culling are reviewed.
Spatial data structure techniques like maintaining of dynamic scenes and
exploiting spatial and temporal coherences are reviewed.1. Introduction.............................................................................................................1 2. Visibility Problem...................................................................................................3 3. Scene Organization...............................................................................................10 3.1. Bounding Volume Hierarchies and Scene Graphs.................................10 3.2. Spatial Data Structures ...............................................................................13 3.3. Regular Grids...............................................................................................14 3.4. Quadtrees and Octrees ...............................................................................15 3.5. KD-Trees.......................................................................................................20 3.6. BSP-Trees......................................................................................................23 3.7. Exploiting Spatial and Temporal Coherence ..........................................27 3.8. Dynamic Scenes...........................................................................................30 3.9. Summary ......................................................................................................34 4. View Frustum Culling .........................................................................................35 4.1. View Frustum Construction ......................................................................36 4.2. View Frustum Test......................................................................................37 4.3. Hierarchical View Frustum Culling .........................................................41 4.4. Optimizations ..............................................................................................42 4.5. Summary ......................................................................................................44 5. Occlusion Culling .................................................................................................45 5.1. Fundamental Concepts...............................................................................45 5.2. Occluder Selection.......................................................................................46 5.3. Hardware Occlusion Queries....................................................................49 5.4. Object-Space Methods ................................................................................50 5.5. Image-Space Methods ................................................................................55 5.6. Summary ......................................................................................................64 6. Conclusion.............................................................................................................66 References .................................................................................................................... 7
A Survey of Methods for Converting Unstructured Data to CSG Models
The goal of this document is to survey existing methods for recovering CSG
representations from unstructured data such as 3D point-clouds or polygon
meshes. We review and discuss related topics such as the segmentation and
fitting of the input data. We cover techniques from solid modeling and CAD for
polyhedron to CSG and B-rep to CSG conversion. We look at approaches coming
from program synthesis, evolutionary techniques (such as genetic programming or
genetic algorithm), and deep learning methods. Finally, we conclude with a
discussion of techniques for the generation of computer programs representing
solids (not just CSG models) and higher-level representations (such as, for
example, the ones based on sketch and extrusion or feature based operations).Comment: 29 page
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