激光与光电子学进展, 2019, 56 (4): 040002, 网络出版: 2019-07-31 复制并引用该论文  本文已被引 8 次  被引通知

图 & 表

图 1. 3D数据采集系统。(a)车载系统;(b)固定式扫描仪;(c)手推车式采集系统;(d)背负式采集系统

Fig. 1. Three-dimensional data acquisition systems. (a) Vehicle system; (b) fixed scanner; (c) trolley type acquisition system; (d) backpack type acquisition system

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图 2. 场景理解示例。(a)图像语义分割;(b)点云目标检测;(c)点云语义分割

Fig. 2. Examples of scene understanding. (a) Semantic segmentation of image; (b) target detection of point cloud; (c) semantic segmentation of point cloud

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图 3. 2D图像受环境因素影响示例。(a)目标严重遮挡;(b) a图语义分割结果;(c)目标受光照影响;(d) c图语义分割结果

Fig. 3. Examples of 2D images affected by environmental factors. (a) Severe occlusion of target; (b) semantic segmentation of (a); (c) target affected by light; (d) semantic segmentation of (c)

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图 4. 3D彩色点云室外场景语义分割

Fig. 4. Semantic segmentation of semantic 3D color point cloud of outdoor scene

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图 5. 小区彩色点云图像

Fig. 5. Color point cloud image of community

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图 6. 背负式全景影像移动轨迹(黄色线条)

Fig. 6. Trajectory (yellow lines) of collision-based panorama image

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图 7. 室外场景的完整彩色点云

Fig. 7. Complete colorful point cloud of outdoor scene

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图 8. 点云特征提取及点云分割研究现状

Fig. 8. Research status of point cloud feature extraction and point cloud segmentation

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表 1场景理解不同数据的优缺点对比

Table1. Comparison of advantages and disadvantages of different data in scenario

Name Instance Advantage characteristic Disadvantage 2D RGB image 2D information,color textures, etc. Image is susceptible toillumination, lacks depthinformation, cannot directlyacquire geometric information. Point cloud databased on image3D reconstruction 3D scenes, color textures andother sparse point clouds,with depth information, distance. Point cloud is susceptible tolight and environment, andreconstruction information is lost. Kinect-basedRGB-D pointcloud data 3D indoor small scene, densepoint cloud, only for closerange, with depth. Point cloud is susceptible tolight, only for indoor scenes,small field of view. Point cloud databased onvehicle lidar High-precision, dense point clouds,and three-dimensional spatialinformation and intensity information.Less affected by environmental factors. Limited by platform, only point cloudscenes with linear road trajectoriescan be generated. Unable to extractscene color. information Point cloud databased onstatic lidar 3D outdoor scene, high-precision,dense point cloud, 3D spatialinformation and intensity. Collection device cannot bemoved, and point cloudscene is incomplete. Point cloud databased onaerial lidar 3D outdoor scene, high-precisioninformation, sparse point cloud,suitable for large-scalerough modeling. Unable to reproduce ground details,laser radar cannot extractscene color information. Collision-basedpanoramic imageand laser pointcloud fusion data 3D outdoor scenes, high-precisioninformation, dense point clouds,mobile modeling, full-frame3D space, color and intensity. Scene color informationcannot be extracted by thelaser point cloud alone.

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表 2噪声点的滤波方法比较

Table2. Comparison of noise filtering methods

Name Principle Characteristic Throughfilter According to point cloud, threshold should be set inrange of X, Y, and Z axes, and then threshold isfiltered to remove points that are out of range. Fast, but not accurate enough,mostly used for rough processingin the first step. Statisticalfiltering Noise point is removed according to point density.The denser the points at certain area, the larger theamount of information. The noise information is useless,and amount of information is small. By calculating averagedistance of each point to its nearest k points,Gaussian distribution of distances of all pointsin point cloud is obtained to eliminate noise. The effect is better than the pass-throughfilter, which can accurately filter out noisepoints inside bounding box. Radiusfiltering Given a radius threshold, calculate the number ofpoints in its radius for each point in the point cloud.When the number is greater than a given number ofthresholds, point is retained, otherwise point istaken as a noise point and rejected. Filter out noise points insidebounding box at a faster rate.

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表 3地面滤波方法比较

Table3. Comparison of ground filter methods

Name Principle Characteristic Elevation basedfiltering method According to point distribution filtering in point cloud,manually set or adaptively find z-direction threshold,and filter out point where z-value is less than thresholdin point cloud as ground. Fast, low robustness. Model basedfiltering method Select a model to fit ground (such as RANSAC-basedplanar model, CSF cloth simulation) and usefitted inner point as ground point. The algorithm is suitable forspecific environments, and the robustness ispoor, but the filtering effect is relatively good. Filtering methodbased onregion growing Taking normal vector direction of point as criterion forregional growth, first adaptively find the point that ismost likely to be the ground. Based on this, based onangular difference between normal vector direction of itsneighborhood point and its normal vector direction, it isjudged whether it grows or not. Continuous iteration tofind all ground points. When the ground is not undulating,the algorithm can separate the ground well,but the time and space cost are relatively large. Method based onwindow movement Points distributed on ground should be of a continuousnature. Set a suitable window size to find the lowestpoint in current window. Then set threshold by thelowest point calculation model and filter out all pointswhere elevation difference exceeds threshold. Faster, but the window size is toodependent on manual settings, andonly local features are considered. Triangulationbased filteringmethod The discrete points are connected according to a certainrule into a plurality of triangles covering entire areawithout overlapping each other to form an irregulartriangular network. The sparse TIN is generated by theseed point, and slope of model is analyzed to performinitial segmentation, and slope is eliminated. Largetriangular regions, and then through connectivityanalysis to obtain features such as elevationdifferences for each segment. It avoids data redundancy when terrain isflat, but data structure is complexand space complexity is high.

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表 4常用的点云特征描述方法及比较

Table4. Common methods and comparison of point cloud feature descriptions

Name Type Principle Characteristic Spinimage Localfeature Count projection coordinates of allvertices to base plane to obtain descriptor. Resistance to rigid transformationand background interference;sensitive to density changes. 3DSC Localfeature Counting number of points in different grids ofspherical neighborhoods to obtain descriptors. Strong discrimination, anti-noise. PFH Localfeature Parameterize the spatial difference between pointand neighborhood and form a multi-bit histogram. Highly robust to point cloud densitychanges; high computational complexity. FPFH Localfeature Recalculate K neighborhood by calculatingtuple of the query point and its neighborscompared to the PFH. Retains most of recognition capabilitiesof PFH, and reduces computationalcomplexity compared to PFH. SHOT Localfeature Spherical neighborhood rasterization, construct ahistogram according to the angle of normal vector,and then concatenate histogram. Descriptive, anti-noise;large amount of calculation,sensitive to density changes. ESF Globalfeature Describe angle, distance, and triangle area ofthree random points in a point cloud. No need for pre-processing,strong feature description. VFH Globalfeature Add pilot direction to relative normalcalculation after extending FPFH. Strongly discernible.

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李勇, 佟国峰, 杨景超, 张立强, 彭浩, 高华帅. 三维点云场景数据获取及其场景理解关键技术综述[J]. 激光与光电子学进展, 2019, 56(4): 040002. Yong Li, Guofeng Tong, Jingchao Yang, Liqiang Zhang, Hao Peng, Huashuai Gao. 3D Point Cloud Scene Data Acquisition and Its Key Technologies for Scene Understanding[J]. Laser & Optoelectronics Progress, 2019, 56(4): 040002.