VOXELIZATION TECHNIQUES: DATA SEGMENTATION AND DATA MODELLING FOR 3D BUILDING MODELS
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Abstract. Voxelization of data is discretizing the 3D space, in which the simplest form is a single voxel. There is a large number of publications that are related to voxelization. However, this paper focuses on the voxelization technique implemented in 3D building modelling. This paper aims to get the development idea of the voxelization technique throughout these past years to determine the suitable technique and method for including a 3D voxelized building model in Computational Fluid Dynamics (CFD). From the search and analysis, it is found that this technique is not only related to data modelling of the 3D voxelized model; the voxelization technique can also be utilized in the data segmentation process. First, for the data segmentation, the voxelization technique is implemented to manage the large amount of point cloud data that were obtained from the 3D scanner and sensors, which is done by reducing the number of data to avoid data redundancy and unused data using each of the voxels that exist in that environment. Second, for data modelling, popular input data to generate the 3D voxelized model is also in the form of a point cloud. However, there are still other forms, such as line and surface. Nevertheless, this paper reviews the voxelized technique in building modelling despite some data segmentation. The review shows various input data, applications, and techniques associated with the voxelization process based on building model generation. However, there is still room for improvement that allows the 3D model to be modelled in the voxelized form in the CFD domain.Keywords:
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In this paper we present a new algorithm that extends Seitz and Dyer's voxel coloring algorithm. This paper explores a method of voxel coloring which scans only the voxels on the surface of 3D reconstructed scene and the voxels of the nearest neighbors of the 3d reconstructed scene. This method reduces significantly the number of voxels to be processed and retaining the accuracy of actual voxel coloring method. With our algorithm, the time complexity rather than being O(n3), reduces to O(number of voxels on the surface of reconstructed scene). The significant advantage of our algorithm can be seen while increasing the number of voxels (or reducing the size of voxels), for greater accuracy of reconstructed results. We present efficiency measurements for comparisons. Our algorithm can also be used with the Voxel coloring method reported in, increasing significantly the speed of the scan.
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Abstract. Voxels have been used in various application domains successfully for the last several decades. Their main advantage is the underlying discrete data structure allowing to reliably work with surrounding voxels all the time. In this paper, capabilities of the Unity game engine for voxels management and geometry voxelisation are assessed, where 4 native solutions and 7 open-source projects written for Unity are investigated. Although many voxel-based options exist in Unity, they only deal with one part related to voxels. Therefore, the available capabilities to voxelise, visualise, structure and export voxels are combined and extended with the goal of successfully processing large 3D models and geometries. Many voxel visualisation techniques are investigated including mesh, VFX, point clouds and SVO, which have distinctive benefits in various aspects. Possibilities to structure voxels for effective management in simulations and other tasks are shown. Also, it is enabled to export voxels as point clouds and to the Postgres database for further processing, spatial analysis and distribution. One of the main conclusions is the lack of support for state-of-the-art voxel data structures, where the presented platform can easily be extended to support any. This platform can be used by people who deal with 3D discrete data, require voxelising 3D data, visualise voxels in different ways and technologies, as well as manage more efficiently sparsely occupied voxels.
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A 3D laser scanneris contactlessly,with overall scanning characteristics,are suited for collecting complete and detailed shape,size,color,and texture information from cultural relics.This imformatiom can be used in further research as well as in in repair work.This paper focuses on the use of 3D laser scanning onChuWangLing's tomb passage and the architectures around the tomb passages,The 3D laser point cloud is used to reconstruct the ChuWangLing's stomb passages and surrounding architectures.Finally DLG is used to creat the complete 3D model.
Laser Scanning
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3D Reconstruction
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The aim of this work is to visualize 3D objects in volume data with minimum numbers of user-defined model or parameters. In this report, we propose a novel method that utilizes the distances along the optimum paths between a seed voxel in a target object and other voxels. The distance is defined using gradient between adjacent voxels or using difference between the seed voxel and other voxels along the optimum path. The optimization of a path is carried out by selecting the path where the largest value of absolute gradient or difference along a path is minimum, and the distance of each voxel is the largest value along the optimum path. The visualization is performed by rendering the volume where the initial voxel values are replaced with the distances. By the proposed method, the volume visualization can be accomplished only by setting a seed voxel in the target object. From experiments for visualizing human embryos obtained with MR microscopy, we confirmed that the proposed method successfully visualized the objects.
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Abstract Point cloud segmentation is an essential task in three-dimensional (3D) vision and intelligence. It is a critical step in understanding 3D scenes with a variety of applications. With the rapid development of 3D scanning devices, point cloud data have become increasingly available to researchers. Recent advances in deep learning are driving advances in point cloud segmentation research and applications. This paper presents a comprehensive review of recent progress in point cloud segmentation for understanding 3D indoor scenes. First, we present public point cloud datasets, which are the foundation for research in this area. Second, we briefly review previous segmentation methods based on geometry. Then, learning-based segmentation methods with multi-views and voxels are presented. Next, we provide an overview of learning-based point cloud segmentation, ranging from semantic segmentation to instance segmentation. Based on the annotation level, these methods are categorized into fully supervised and weakly supervised methods. Finally, we discuss open challenges and research directions in the future.
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3D reconstruction has been attracting increasing attention in the past few years. With the surge of deep neural networks, the performance of 3D reconstruction has been improved significantly. However, the voxel reconstructed by extant approaches usually contains lots of noise and leads to heavy computation. In this paper, we define a new voxel representation, named Weighted Voxel. It provides more abundant information, facilitating the subsequent learning and generalization steps. Unlike regular voxel which consists of zero-one, the proposed Weighted Voxel makes full use of the structure information of voxels. Experimental results demonstrate that Weighted Voxel not only performs better in reconstruction but also takes less time in training.
Representation
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This study proposes a method generating a 3D model of furniture from 3D point cloud data of a room captured by RGBD camera in order to realize the layout simulation of the real room with furniture.
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3d printed
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The voxel model discretizes the solid model with voxels, and can represent the local physical properties of the model such as materials, colors, and materials. For the problem of having low operation efficiency and taking up a lot of storage space caused by the huge amount of voxel model data, this paper proposes a Line-Based Structure for the voxel model. In this structure, the voxel model is treated as an ordered set of Lines, which is formed of voxels. It builds the correlation of independent voxels. Based on this structure, this paper achieves a voxel batch operation method and proposed voxel data fast indexing function, improving the operation efficiency of the voxel model. Then using the state of each position in the space to represent whether the voxel exists in that position, the voxel model data compressing and decompressing methods are proposed. This achieves effective compression of large data voxel models and provides a quick preview function of the model, reducing the voxel model storage space. The effect of the method in this paper is verified through experiments.
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Line (geometry)
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Abstract. In this paper, a three steps segmentation approach is proposed in order to create 3D models from point clouds acquired by TLS inside buildings. The three scales of segmentation are floors, rooms and planes composing the rooms. First, floor segmentation is performed based on analysis of point distribution along Z axis. Then, for each floor, room segmentation is achieved considering a slice of point cloud at ceiling level. Finally, planes are segmented for each room, and planes corresponding to ceilings and floors are identified. Results of each step are analysed and potential improvements are proposed. Based on segmented point clouds, the creation of as-built BIM is considered in a future work section. Not only the classification of planes into several categories is proposed, but the potential use of point clouds acquired outside buildings is also considered.
Ceiling (cloud)
Base (topology)
Section (typography)
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