1,902 research outputs found
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
This paper proposes a segmentation-free, automatic and efficient procedure to
detect general geometric quadric forms in point clouds, where clutter and
occlusions are inevitable. Our everyday world is dominated by man-made objects
which are designed using 3D primitives (such as planes, cones, spheres,
cylinders, etc.). These objects are also omnipresent in industrial
environments. This gives rise to the possibility of abstracting 3D scenes
through primitives, thereby positions these geometric forms as an integral part
of perception and high level 3D scene understanding.
As opposed to state-of-the-art, where a tailored algorithm treats each
primitive type separately, we propose to encapsulate all types in a single
robust detection procedure. At the center of our approach lies a closed form 3D
quadric fit, operating in both primal & dual spaces and requiring as low as 4
oriented-points. Around this fit, we design a novel, local null-space voting
strategy to reduce the 4-point case to 3. Voting is coupled with the famous
RANSAC and makes our algorithm orders of magnitude faster than its conventional
counterparts. This is the first method capable of performing a generic
cross-type multi-object primitive detection in difficult scenes. Results on
synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201
An efficient multi-resolution framework for high quality interactive rendering of massive point clouds using multi-way kd-trees
We present an efficient technique for out-of-core multi-resolution construction and high quality interactive visualization of massive point clouds. Our approach introduces a novel hierarchical level of detail (LOD) organization based on multi-way kd-trees, which simplifies memory management and allows control over the LOD-tree height. The LOD tree, constructed bottom up using a fast high-quality point simplification method, is fully balanced and contains all uniformly sized nodes. To this end, we introduce and analyze three efficient point simplification approaches that yield a desired number of high-quality output points. For constant rendering performance, we propose an efficient rendering-on-a-budget method with asynchronous data loading, which delivers fully continuous high quality rendering through LOD geo-morphing and deferred blending. Our algorithm is incorporated in a full end-to-end rendering system, which supports both local rendering and cluster-parallel distributed rendering. The method is evaluated on complex models made of hundreds of millions of point sample
A software tool for the semi-automatic segmentation of architectural 3D models with semantic annotation and Web fruition
The thorough documentation of Cultural Heritage artifacts is a fundamental concern for management and preservation. In this context, the semantic segmentation and annotation of 3D models of historic buildings is an important modern topic. This work describes a software tool currently under development, for interactive and semi-automatic segmentation, characterization, and annotation of 3D models produced by photogrammetric surveys. The system includes some generic and well-known segmentation approaches, such as region growing and Locally Convex Connected Patches segmentation, but it also contains original code for specific semantic segmentation of parts of buildings, in particular straight stairs and circular-section columns. Furthermore, a method for automatic wall-surface characterization is devoted to rusticated-ashlar detection, in view of masonry-unit segmentation. The software is modular, so allowing easy expandability. It also has tools for data encoding into formats ready for model fruition by Web technologies. These results were partly obtained in collaboration with Corvallis SPA (Padua-Italy, http://www.corvallis.it)
The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker
Reliable collision avoidance is one of the main requirements for autonomous
driving. Hence, it is important to correctly estimate the states of an unknown
number of static and dynamic objects in real-time. Here, data association is a
major challenge for every multi-target tracker. We propose a novel multi-target
tracker called Greedy Dirichlet Process Filter (GDPF) based on the
non-parametric Bayesian model called Dirichlet Processes and the fast posterior
computation algorithm Sequential Updating and Greedy Search (SUGS). By adding a
temporal dependence we get a real-time capable tracking framework without the
need of a previous clustering or data association step. Real-world tests show
that GDPF outperforms other multi-target tracker in terms of accuracy and
stability
Developing a Semantic-Driven Hybrid Segmentation Method for Point Clouds of 3D Shapes
With the rapid development of point cloud processing technologies and the availability of a wide range of 3D capturing devices, a geometric object from the real world can be directly represented digitally as a dense and fine point cloud. Decomposing a 3D shape represented in point cloud into meaningful parts has very important practical implications in the fields of computer graphics, virtual reality and mixed reality. In this paper, a semantic-driven automated hybrid segmentation method is proposed for 3D point cloud shapes. Our method consists of three stages: semantic clustering, variational merging, and region remerging. In the first stage, a new feature of point cloud, called Local Concave-Convex Histogram, is introduced to first extract saddle regions complying with the semantic boundary feature. All other types of regions are then aggregated according to this extracted feature. This stage often leads to multiple over-segmentation convex regions, which are then remerged by a variational method established based on the narrow-band theory. Finally, in order to recombine the regions with the approximate shapes, order relation is introduced to improve the weighting forms in calculating the conventional Shape Diameter Function. We have conducted extensive experiments with the Princeton Dataset. The results show that the proposed algorithm outperforms the state-of-the-art algorithms in this area. We have also applied the proposed algorithm to process the point cloud data acquired directly from the real 3D objects. It achieves excellent results too. These results demonstrate that the method proposed in this paper is effective and universal
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
Subdivision surface fitting to a dense mesh using ridges and umbilics
Fitting a sparse surface to approximate vast dense data is of interest for many applications: reverse engineering, recognition and compression, etc. The present work provides an approach to fit a Loop subdivision surface to a dense triangular mesh of arbitrary topology, whilst preserving and aligning the original features. The natural ridge-joined connectivity of umbilics and ridge-crossings is used as the connectivity of the control mesh for subdivision, so that the edges follow salient features on the surface. Furthermore, the chosen features and connectivity characterise the overall shape of the original mesh, since ridges capture extreme principal curvatures and ridges start and end at umbilics. A metric of Hausdorff distance including curvature vectors is proposed and implemented in a distance transform algorithm to construct the connectivity. Ridge-colour matching is introduced as a criterion for edge flipping to improve feature alignment. Several examples are provided to demonstrate the feature-preserving capability of the proposed approach
- …