TSDF voxel reconstruction with adaptive Gaussian kernel for heterogeneous-density LiDAR, showing competitive results on CARLA synthetic and KITTI real data versus prior surface methods.
Computational Fluid Dynamics on 3D Point Set Surfaces
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Computational fluid dynamics (CFD) in many cases requires designing 3D models manually, which is a tedious task that requires specific skills. In this paper, we present a novel method for performing CFD directly on scanned 3D point clouds. The proposed method builds an anisotropic volumetric tetrahedral mesh adapted around a point-sampled surface, without an explicit surface reconstruction step. The surface is represented by a new extended implicit moving least squares (EIMLS) scalar representation that extends the definition of the function to the entire computational domain, which makes it possible for use in immersed boundary flow simulations. The workflow we present allows us to compute flows around point-sampled geometries automatically. It also gives a better control of the precision around the surface with a limited number of computational nodes, which is a critical issue in CFD.
fields
cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
3D Surface Reconstruction from Voxel-based Lidar Data
TSDF voxel reconstruction with adaptive Gaussian kernel for heterogeneous-density LiDAR, showing competitive results on CARLA synthetic and KITTI real data versus prior surface methods.