402 research outputs found
A knowledge-based approach for the extraction of machining features from solid models
Computer understanding of machining features such as holes and pockets is
essential for bridging the communication gap between Computer Aided Design and
Computer Aided Manufacture. This thesis describes a prototype machining feature
extraction system that is implemented by integrating the VAX-OPS5 rule-based
artificial intelligence environment with the PADL-2 solid modeller. Specification of
original stock and finished part geometry within the solid modeller is followed by
determination of the nominal surface boundary of the corresponding cavity volume
model by means of Boolean subtraction and boundary evaluation. The boundary model
of the cavity volume is managed by using winged-edge and frame-based data
structures. Machining features are extracted using two methods : (1) automatic feature
recognition, and (2) machine learning of features for subsequent recognition. [Continues.
Polylidar3D -- Fast Polygon Extraction from 3D Data
Flat surfaces captured by 3D point clouds are often used for localization,
mapping, and modeling. Dense point cloud processing has high computation and
memory costs making low-dimensional representations of flat surfaces such as
polygons desirable. We present Polylidar3D, a non-convex polygon extraction
algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data),
organized point clouds (e.g., range images), or user-provided meshes.
Non-convex polygons represent flat surfaces in an environment with interior
cutouts representing obstacles or holes. The Polylidar3D front-end transforms
input data into a half-edge triangular mesh. This representation provides a
common level of input data abstraction for subsequent back-end processing. The
Polylidar3D back-end is composed of four core algorithms: mesh smoothing,
dominant plane normal estimation, planar segment extraction, and finally
polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU
multi-threading and GPU acceleration when available. We demonstrate
Polylidar3D's versatility and speed with real-world datasets including aerial
LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds
for road surface detection, and RGBD cameras for indoor floor/wall detection.
We also evaluate Polylidar3D on a challenging planar segmentation benchmark
dataset. Results consistently show excellent speed and accuracy.Comment: 40 page
Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds
Urban reconstruction, with an emphasis on man-made structure modeling, is an active research area with broad impact on several potential applications. Urban reconstruction combines photogrammetry, remote sensing, computer vision, and computer graphics. Even though there is a huge volume of work that has been done, many problems still remain unsolved. Automation is one of the key focus areas in this research. In this work, a fast, completely automated method to create 3D watertight building models from airborne LiDAR (Light Detection and Ranging) point clouds is presented. The developed method analyzes the scene content and produces multi-layer rooftops, with complex rigorous boundaries and vertical walls, that connect rooftops to the ground. The graph cuts algorithm is used to separate vegetative elements from the rest of the scene content, which is based on the local analysis about the properties of the local implicit surface patch. The ground terrain and building rooftop footprints are then extracted, utilizing the developed strategy, a two-step hierarchical Euclidean clustering. The method presented here adopts a divide-and-conquer scheme. Once the building footprints are segmented from the terrain and vegetative areas, the whole scene is divided into individual pendent processing units which represent potential points on the rooftop. For each individual building region, significant features on the rooftop are further detected using a specifically designed region-growing algorithm with surface smoothness constraints. The principal orientation of each building rooftop feature is calculated using a minimum bounding box fitting technique, and is used to guide the refinement of shapes and boundaries of the rooftop parts. Boundaries for all of these features are refined for the purpose of producing strict description. Once the description of the rooftops is achieved, polygonal mesh models are generated by creating surface patches with outlines defined by detected vertices to produce triangulated mesh models. These triangulated mesh models are suitable for many applications, such as 3D mapping, urban planning and augmented reality
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