With the continuous progress of computer vision and graphics technology, surface reconstruction has become a key research direction in this field. This study provides a comprehensive and in-depth analysis of three tra...
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作者：
Ma, WeiLi, QingquanShenzhen Univ
Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China Shenzhen Univ
Shenzhen Key Lab Spatial Smart Sensing & Serv Shenzhen 518060 Peoples R China Shenzhen Univ
Key Lab Geoenvironm Monitoring Coastal Zone Natl Shenzhen 518060 Peoples R China
Automatic ground filtering is an essential step for Digital Elevation Model (DEM) generation, which has significant application value. However, extraction and classification of ground points from the Light Detection a...
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Automatic ground filtering is an essential step for Digital Elevation Model (DEM) generation, which has significant application value. However, extraction and classification of ground points from the Light Detection and Ranging (LiDAR) data, especially in multitudinous terrain situations, is a challenging task because it is difficult to determine the set of optimal parameters for removing various non-ground features. In this paper, a new ground filtering technique based on an improved ball Pivot algorithm (BPA) is proposed. At the beginning, the LiDAR point cloud dataset was divided into different subsets based on the 2D regular grid. The lowest point in each grid was selected as the seed point to build a single-layer surface. After that, the improved BPA was executed to remove points on the higher location. Then, the rest of the points were calculated and selected as a new seed point according to the spatial relationship with the initial surface. Finally, non-ground points were filtered by means of improved BPA traversing all the grids. Our experimental results on the Benchmark dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) Working Group III/3 showed high accuracy (with a mean kappa coefficient over 80%) in terms of completeness, correctness, and quality for DEM generation. The experimental results demonstrated the proposed method is robust to various terrain situations, as it is more effective and feasible for ground filtering.
We present a new high quality tessellation method for implicit surfaces in this paper. The approach can handle arbitrary implicit functions and dynamic implicit surfaces based on skeletal primitives. We first samples ...
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ISBN:
(纸本)0769524737
We present a new high quality tessellation method for implicit surfaces in this paper. The approach can handle arbitrary implicit functions and dynamic implicit surfaces based on skeletal primitives. We first samples the implicit surface uniformly using particle fission and floating, then reconstructs a triangular mesh from the sample points using ball pivoting algorithm (BPA). Finally, we subdivide the reconstructed surface using a 1 to 4 subdivision scheme to obtain the high quality implicit surface tessellation.
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