The Point Cloud Library (or PCL) is a large scale, open project  for 2D/3D image and point cloud processing. The PCL framework contains numerous state-of-the art algorithms including filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation. These algorithms can be used, for example, to filter outliers from noisy data, stitch 3D point clouds together, segment relevant parts of a scene, extract keypoints and compute descriptors to recognize objects in the world based on their geometric appearance, and create surfaces from point clouds and visualize them -- to name a few.
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Kudela, László; Kollmannsberger, Stefan; Almac, Umut; Rank, Ernst: Direct structural analysis of domains defined by point clouds (2020)
- Sebastian Lamprecht: Pyoints: A Python package for point cloud, voxel and raster processing (2019) not zbMATH
- Sun, Da; Liao, Qianfang; Stoyanov, Todor; Kiselev, Andrey; Loutfi, Amy: Bilateral telerobotic system using type-2 fuzzy neural network based moving horizon estimation force observer for enhancement of environmental force compliance and human perception (2019)
- Zhong, Sikai; Zhong, Zichun; Hua, Jing: Surface reconstruction by parallel and unified particle-based resampling from point clouds (2019)
- Sveier, Aksel; Kleppe, Adam Leon; Tingelstad, Lars; Egeland, Olav: Object detection in point clouds using conformal geometric algebra (2017)
- Ansari, Zafar Ahmed; Harit, Gaurav: Nearest neighbour classification of Indian sign language gestures using kinect camera (2016) ioport
- Wilkowski, Artur; Kornuta, Tomasz; Stefańczyk, Maciej; Kasprzak, Włodzimierz: Efficient generation of 3D surfel maps using RGB-D sensors (2016)
- Lehment, Nicolas; Kaiser, Moritz; Rigoll, Gerhard: Using segmented 3D point clouds for accurate likelihood approximation in human pose tracking (2013) ioport