1,060 research outputs found
Occlusion Modeling for Coherent Echo Data Simulation:A Comparison Between Ray-Tracing and Convex-Hull Methods
The ability to simulate realistic coherent datasets for synthetic aperture imaging systems is crucial for the design, development and evaluation of the sensors and their signal processing pipelines, machine learning algorithms and autonomy systems. In the case of synthetic aperture sonar (SAS), collecting experimental data is expensive and it is rarely possible to obtain ground truth of the sensor’s path, the speed of sound in the medium, and the geometry of the imaged scene. Simulating sonar echo data allows signal processing algorithms to be tested with known ground truth, enabling rapid and inexpensive development and evaluation of signal processing algorithms. The de-facto standard for simulating conventional high-frequency (i.e., > 100 kHz) SAS echo data from an arbitrary sensor, path and scene is to use a point-based or facet-based diffraction model. A crucial part of this process is acoustic occlusion modeling. This article describes a SAS simulation pipeline and compares implementations of two occlusion methods; ray-tracing, and a newer approximate method based on finding the convex hull of a transformed point cloud. The full capability of the simulation pipeline is demonstrated using an example scene based on a high-resolution 3D model of the SS Thistlegorm shipwreck which was obtained using photogrammetry. The 3D model spans a volume of 220 × 130 × 25 m and is comprised of over 30 million facets that are decomposed into a cloud of almost 1 billion points. The convex-hull occlusion model was found to result in simulated SAS imagery that is qualitatively indistinguishable from the ray-tracing approach and quantitatively very similar, demonstrating that use of this alternative method has potential to improve speed while retaining high fidelity of simulation.The convex-hull approach was found to be up to 4 times faster in a fair speed comparison with serial and parallel CPU implementations for both methods, with the largest performance increase for wide-beam systems. The fastest occlusion modeling algorithm was found to be GPU-accelerated ray-tracing over the majority of scene scales tested, which was found to be up to 2 times faster than the parallel CPU convex-hull implementation. Although GPU implementations of convex hull algorithms are not currently readily available, future development of GPU-accelerated convex-hull finding could make the new approach much more viable. However, in the meantime, ray-tracing is still preferable, since it has higher accuracy and can leverage existing implementations for high performance computing architectures for better performance
Finding Convex Hulls Using Quickhull on the GPU
We present a convex hull algorithm that is accelerated on commodity graphics
hardware. We analyze and identify the hurdles of writing a recursive divide and
conquer algorithm on the GPU and divise a framework for representing this class
of problems. Our framework transforms the recursive splitting step into a
permutation step that is well-suited for graphics hardware. Our convex hull
algorithm of choice is Quickhull. Our parallel Quickhull implementation (for
both 2D and 3D cases) achieves an order of magnitude speedup over standard
computational geometry libraries.Comment: 11 page
QuickCSG: Fast Arbitrary Boolean Combinations of N Solids
QuickCSG computes the result for general N-polyhedron boolean expressions
without an intermediate tree of solids. We propose a vertex-centric view of the
problem, which simplifies the identification of final geometric contributions,
and facilitates its spatial decomposition. The problem is then cast in a single
KD-tree exploration, geared toward the result by early pruning of any region of
space not contributing to the final surface. We assume strong regularity
properties on the input meshes and that they are in general position. This
simplifying assumption, in combination with our vertex-centric approach,
improves the speed of the approach. Complemented with a task-stealing
parallelization, the algorithm achieves breakthrough performance, one to two
orders of magnitude speedups with respect to state-of-the-art CPU algorithms,
on boolean operations over two to dozens of polyhedra. The algorithm also
outperforms GPU implementations with approximate discretizations, while
producing an output without redundant facets. Despite the restrictive
assumptions on the input, we show the usefulness of QuickCSG for applications
with large CSG problems and strong temporal constraints, e.g. modeling for 3D
printers, reconstruction from visual hulls and collision detection
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