Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds
| dc.contributor.author | Jakob, Johannes | en_US |
| dc.contributor.author | Buchenau, Christoph | en_US |
| dc.contributor.author | Guthe, Michael | en_US |
| dc.contributor.editor | Bommes, David and Huang, Hui | en_US |
| dc.date.accessioned | 2019-07-11T06:19:34Z | |
| dc.date.available | 2019-07-11T06:19:34Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | A mandatory component for many point set algorithms is the availability of consistently oriented vertex-normals (e.g. for surface reconstruction, feature detection, visualization). Previous orientation methods on meshes or raw point clouds do not consider a global context, are often based on unrealistic assumptions, or have extremely long computation times, making them unusable on real-world data. We present a novel massively parallelized method to compute globally consistent oriented point normals for raw and unsorted point clouds. Built on the idea of graph-based energy optimization, we create a complete kNN-graph over the entire point cloud. A new weighted similarity criterion encodes the graph-energy. To orient normals in a globally consistent way we perform a highly parallel greedy edge collapse, which merges similar parts of the graph and orients them consistently. We compare our method to current state-of-the-art approaches and achieve speedups of up to two orders of magnitude. The achieved quality of normal orientation is on par or better than existing solutions, especially for real-world noisy 3D scanned data. | en_US |
| dc.description.number | 5 | |
| dc.description.sectionheaders | 2D and 3D Reconstruction | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 38 | |
| dc.identifier.doi | 10.1111/cgf.13797 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 163-173 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13797 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13797 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Shape analysis | |
| dc.subject | Theory of computation | |
| dc.subject | Computational geometry | |
| dc.subject | Massively parallel algorithms | |
| dc.title | Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds | en_US |