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.
A Statistical Update of Grid Representations from Range Sensors
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In a wide range of robotic applications, being able to create a 3D model of the surrounding environment is a key feature for autonomous tasks. In this research report, we present a statistical model to perform 3D reconstructions of the environment from range sensors using an occupancy grid. To do so, we take into account all the available information obtained from the sensor, considering the distances traversed by the rays in each cell and seeking to reduce reconstruction errors caused by discretization. The approach has been validated qualitatively using the KITTI dataset.
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.