Abstract
Abstract. Plane segmentation from the point cloud is an important step in various types of geo-information related to human activities. In this paper, we present a new approach to accurate segment planar primitives simultaneously by transforming it into the best matching issue between the over-segmented super-voxels and the 3D plane models. The super-voxels and its adjacent topological graph are firstly derived from the input point cloud as over-segmented small patches. Such initial 3D plane models are then enriched by fitting centroids of randomly sampled super-voxels, and translating these grouped planar super-voxels by structured scene prior (e.g. orthogonality, parallelism), while the generated adjacent graph will be updated along with planar clustering. To achieve the final super-voxels to planes assignment problem, an energy minimization framework is constructed using the productions of candidate planes, initial super-voxels, and the improved adjacent graph, and optimized to segment multiple consistent planar surfaces in the scenes simultaneously. The proposed algorithms are implemented, and three types of point clouds differing in feature characteristics (e.g. point density, complexity) are mainly tested to validate the efficiency and effectiveness of our segmentation method.
Highlights
Detecting planar surfaces from LiDAR and photogrammetry point cloud, due to its vast applications in many areas, has been an active topic in many research communities (Brook et al, 2013)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-2-2020, 2020 XXIV ISPRS Congress (2020 edition) improvements have achieved satisfying 3D plane segmentation results, it always fails as the sensitive model parameters with noise and outliers
This paper develops a simple segmentation strategy that is to transform the plane segmentation issue into the best matching issue between the over-segmented super-voxels and the 3D plane models, improving the robustness to noise and the efficiency of global optimization
Summary
Detecting planar surfaces from LiDAR and photogrammetry point cloud, due to its vast applications in many areas, has been an active topic in many research communities (Brook et al, 2013). Many algorithms and systems have been proposed to the plane segmentation based on the type of input data and objects, making the production of segmentation faster and better. Even though much progress has been successfully achieved, the robust and accurate 3D plane segmentation from the point cloud remains to be a challenging issue, especially for the complex scenes with noise, outliers, and occlusions. Such a process on the acquired massive point clouds can be quite a time consuming, and information of surfaces, boundaries, scene priors (e.g. orthogonality, parallelism) are not preserved or even extracted. This paper proposes a robust and efficient unsupervised method to the segmentation of point cloud acquired from structural scenes
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