Large-scale Point Clouds Research Articles

Complementing TLS Point Clouds by Dense Image Matching

The complementation of terrestrial laser scanning (TLS) by photogrammetric acquisition techniques enables the exploitation of two different measurement principles. TLS with distance measurements can be used for acquiring large-scale point clouds at medium range distances, while image-based surface reconstruction methods enable flexible acquisition with high precision at short distances. Within this paper, an automatic procedure for the combination of photogrammetry and laser scanning is presented. Synthetic images derived from the laser scanning point clouds are used together with camera images in a common Structure-from-Motion (SfM) process. This enables an automatic registration of multiple laser scans. Furthermore, surface information can also be derived from the imagery using dense image matching methods. While the laser scanning data provides the missing scale information, the imagery can complement the dataset to fill gaps, occlusions or to resolve small details. Consequently, large datasets can be connected by registering imagery and laser scans.

A 3D Simplification Method based on Dual Point Sampling

For efficiently processing integration, registration, representation and recognition of large-scale 3D point clouds stored in computer disks or other hardware, it is important to simplify their sheer volume, discarding redundant information and meanwhile preserving the most important information as much as possible. This paper proposes an effective point cloud simplification method which is based on data points sampling. The dual sampling scheme considers both the local details and the overall shape. The local details analysis approach is based on graph-based segmentation, while for the overall shape analysis, the approach voxelizes the model and samples points in terms of the entropy, based on the shape index of vertices. Compared to other simplification methods, this approach reduces the number of vertices in a 3D model described by a point cloud and better preserves local details. We present a number of results to show that the method performs well both visually and quantitatively.

Detection of Surfaces and Edges in Large Point-Clouds Using Region-Growing on Spherical Space

Engineering facilities can be digitized as large-scale point-clouds by using the state-of-art mid-range laser scanners. For utilizing captured data in CAD systems, it is important to convert point-clouds to parametric surfaces. In this paper, we describe a method for robustly extract cylindrical faces and planar faces. Edges and silhouette lines have to be generated to construct bounded faces, but unfortunately points on silhouettes are very noisy in the case of mid-range laser scanners. Our method applies region-growing method on spherical space and improves the robustness. In addition, we enhance the region-growing so that surface regions can be propagated to disconnect points using multiple overlapping point-clouds.

A DATA MANAGEMENT METHOD FOR EFFICIENT SEARCH AND RENDERING OF MULTIPLE LARGE SCALE POINT CLOUDS

Abstract. Recently, with the progress of laser scanning technology, it has become possible to scan several environments such as buildings, roads, and cities easily and at a low cost. There are typically three scanning methods: TLS (Terrestrial Laser Scanning), MMS (Mobile Mapping System), and ALS (Airborne Laser Scanning), and the environments are scanned for measurement, modelling, and simulation etc. Therefore, an efficient and comprehensive data management method, which can handle these point clouds in various applications is strongly required. In this paper, we describe a method of data management for efficient search and rendering of multiple large scale point clouds. First, we define a general point cloud file format including the octree and the quantization structure. Next, the data management method enabling an efficient search and rendering of the point clouds is proposed. Finally, the effectiveness of our method is verified using several actual data sets.

Computation of approximate geodesics on point cloud

In order to compute approximate geodesic efficiently between two points on point cloud,a weighted graph was constructed by splitting point cloud along the Cartesian coordinate axes,thus initial approximate geodesic between any two given points could be computed out using Dijkstra's algorithm.Then the conjugate gradient method was adopted to minimize the energy function defined;finally,approximate geodesic could be obtained after some iterative steps.This proposed algorithm avoids meshing or reconstructing the point cloud to be local or global surface,and it is suitable for computing geodesic on large scale point cloud.

An Integrated Processing Method for Multiple Large-scale Point-Clouds Captured from Different Viewpoints

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

画像特徴量を用いた大規模点群データからの円柱面と矩形面の検出(機械要素,潤滑,設計,生産加工,生産システムなど)

The state-of-the-art mid/long-range laser scanners can capture dense point-clouds from engineering facilities. However, it is very tedious work to manually segment large-scale point-clouds and fit surfaces to the subsets of point-clouds for constructing 3D models. In this paper, we propose a new method to automatically extract cylindrical faces and rectangle faces, which are commonly used for standard parts in engineering facilities. Since it requires a lot of time and memory to search surfaces in large-scale point-clouds, we generate distance images and intensity images from dense point-clouds and estimate canditates of cylindrical and rectangle regions using image processing techniques. Then, we calculate equations of surfaces by applying the region-growing method to the candidate regions, and detect rectangle and cylindrical faces

Extraction of Surface Primitives from Noisy Large-Scale Point-Clouds

Abstract3D shape acquisition of large facilities, such as industrial plants and power plants, has been receiving increasing attention for simulating complicated maintenance and repair tasks. It is widely recognized that model-based planning based on 3D CAD reduces the rework of maintenance tasks to a large extent. The state-of-the-art phase-based laser scanners are very promising to efficiently capture point-clouds of large facilities; because they can produce hundreds of millions point data in several minutes. However, point data captured from the phase-based scanner tend to include large noise components and quite a lot of outliers. In addition, very large memory space is required to generate solid models by processing very large point-clouds. Therefore, it is preferable to develop techniques that can extract surface primitives, such as planes, cylinders, cones, spheres, and tori from very noisy and large-scale point-clouds. This paper introduces a robust streaming smoothing operator for extracting surf...