Research On 3D Point Cloud Segmentation Using Boundary Constraint

Point cloud,which consists of numerous discrete points,is a commonly used 3d description method.However,the raw point cloud is usually inhomogeneous,massive and permuted by noise,which induces great challenges for data processing.Therefore point cloud segmentation which divides points with similar features into one group reveals boundaries of neighboring objects,provides topological information of discrete points and produces new top-level geometric features.The derived information facilitates the surface modeling and interpretation in the subsequent processes.Among the unsupervised point cloud segmentation techniques,the region-growing based one requires fewer parameters and is capable of segmenting multiple planes in a short time,which makes it widely popular in 3d point cloud segmentation.However,this method still suffers from some disadvantages that limit its accuracy and efficiency for segmentation.The point,voxel and supervoxel are normally employed as the segmentation units for region growing based methods,among which the supervoxel based method is more efficient and robust,because it oversegment the point cloud in an irregular way previously.However it also suffers from some problems such as difficulty in aligning the boundary with objects that have diverse point cloud density.Apart from the selection of segmentation units,the region growing based method also lacks adaptive and universal merge criteria to deal with some complex situations.Additionally,the region growing algorithm is a greedy strategy,it only considers local connectivity.A wrong combination or separation may lead to continuous errors.In order to solve the above problems,this paper proposes a boundary constraints based point cloud segmentation method that employs the multi-scale supervoxel as segmentation units.The algorithm first oversegment the point cloud to obtain multi-scale supervoxels,which are consistent with the boundaries of real objects.Secondly,we assess the similarity of adjacent supervoxels to get the weight value of connections,which can be utilized as the basis for connectivity judgment in the subsequent merging process.Finally,each supervoxel is initialized as a segment,and the adjacent segments that meet the condition are merged gradually according to the number of paired boundary supervoxels until the end.The main contributions of this paper are as follows.(1)In this paper,an improved multi-scale supervoxel segmentation method is proposed.Flatness is used to guide the selection of feature representative points in the process of supervoxel generation,which improves the boundary adherence of supervoxels.Meanwhile,the scales of supervoxels are adjusted adaptively according to the planarity of surface,which increase the robustness of feature estimation and solves the problem that the scale of supervoxel located in high density area is too small to extract accurate features.(2)This paper proposes a hierarchical method to measure supervoxel connectivity based on smoothness,tangent distance as well as curvature difference.Different calculation standards are adopted for different types of connections,which makes the process of region merging more reasonable and reduces over-segmentation errors.(3)An insequential scheme is employed to grow the regions without selecting the seed points.The number of paired boundary supervoxels is the basis for judging the fusibility of adjacent segments,which reduces the under-segmentation errors.In addition,the matching problem of boundary supervoxels is formulated as the problem of maximum matching for bipartite graphs,and the Hungarian algorithm is introduced to obtain the global optimal solution,which improves the robustness of the algorithm.The algorithms proposed in this paper are tested and verified under simulation,indoor and outdoor datasets,and compared with several representative algorithms.Experimental results show that the algorithm in this paper achieves better segmentation accuracy.