A morphological approach to the design of complex objects

The surface-trajectory model gives a solution to some of the problems presented by the general geometric models where the design of an object is separated from its manufacture. In fact, in this model, the internal representation of objects is made up of machining trajectories. As the display systems usually need triangles to represent the objects, a process of triangulation is needed to visualize them. In other words, a secondary surface model is needed to display the objects. The following is a geometric model that, maintaining the philosophy of the surface-trajectory model, encapsulates the calculation of the machining process from the formal framework that provides the set theory and the mathematical morphology. The model addresses the process of designing objects by assimilation of a machining process by giving solutions to the design of complex objects and an arithmetic to support the generation of trajectories of manufacturing. The design process is similar to the craft work of sculptors designing their pieces by hand with their tools. It also gives a direct solution to the problems of the trajectory generation since they are already defined at the design phase. The model is generic and robust as there are no special cases or complex objects in which the model does not provide a correct solution. It also naturally incorporates the realistic display of the machined objects in a quickly and accurately way

Software Testbed for Selective Laser Sintering

Computer software plays an important role in the implementation of Solid Freeform Fabrication (SFF) technologies. This paper describes a software testbed for processing part geometry for a particular SFF technology, selective laser sintering (SLS), that is built around the separation of the slicing and rasterization operations to accommodate geometric information from a variety of sources. The paper also discusses the process control software being developed for a new high-temperat rkstation for SLS of metal powders. This program features a high-resolution data rmat, the ability to interpolate to achieve a desired resolution, and a menu-driven user interface with graphical feedback and process simulation capabilities.Mechanical Engineerin

PYRO-NN: Python Reconstruction Operators in Neural Networks

Purpose: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the CT reconstruction as a known operator into a neural network. However, most of the approaches presented lack an efficient CT reconstruction framework fully integrated into deep learning environments. As a result, many approaches are forced to use workarounds for mathematically unambiguously solvable problems. Methods: PYRO-NN is a generalized framework to embed known operators into the prevalent deep learning framework Tensorflow. The current status includes state-of-the-art parallel-, fan- and cone-beam projectors and back-projectors accelerated with CUDA provided as Tensorflow layers. On top, the framework provides a high level Python API to conduct FBP and iterative reconstruction experiments with data from real CT systems. Results: The framework provides all necessary algorithms and tools to design end-to-end neural network pipelines with integrated CT reconstruction algorithms. The high level Python API allows a simple use of the layers as known from Tensorflow. To demonstrate the capabilities of the layers, the framework comes with three baseline experiments showing a cone-beam short scan FDK reconstruction, a CT reconstruction filter learning setup, and a TV regularized iterative reconstruction. All algorithms and tools are referenced to a scientific publication and are compared to existing non deep learning reconstruction frameworks. The framework is available as open-source software at \url{https://github.com/csyben/PYRO-NN}. Conclusions: PYRO-NN comes with the prevalent deep learning framework Tensorflow and allows to setup end-to-end trainable neural networks in the medical image reconstruction context. We believe that the framework will be a step towards reproducible researchComment: V1: Submitted to Medical Physics, 11 pages, 7 figure

Robust regularized set operations on polyhedra

Journal ArticleThis paper describes a provably correct and robust implementation of regularized set operations on polyhedral objects. Although the algorithm described here does not assume manifold polyhedra and handles all possible degenerate cases, it turns out to be quite simple. The geometric operations and relations are computed with floating point arithmetic which is efficient but not necessarily precise. To ensure that the results are still consistent we implemented a test that detects when dependent decisions contradict each other. The consistency test is relatively simple and can be carried out locally without having to reason about the logical dependencies of the geometric relations. The logical structure and the efficiency of the algorithm are barely influenced by the consistency test which makes this approach well suited for interactive modeling systems on modern workstations

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