Point Cloud Processing

With the growing availability of depth cameras and lidar, the processing of the resulting point clouds has become its own sub-area of Computer Vision.

Computer visionIllustration of the probabilistic modelling of point clouds using latent variables.Data from these sensors is usually sparse, thus requires densification. Also, the position and orientation are often only partly known and a registration of the measurements in a joint reference system is required.

Finally, the point cloud needs to be split into object-related measurements, i.e., 3D segmentation is needed. We approach these problems using novel deep learning methodology based on normalized CNNs and 3D fusion of deep 2D segmentations, as well as probabilistic modelling.

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A selection of three papers

Abdelrahman Eldesokey, Michael Felsberg, Fahad Shahbaz Khan (2019)

British Machine Vision Conference 2018, BMVC 2018 Continue to DOI

Felix Järemo-Lawin, Martin Danelljan, Patrik Tosteberg, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg (2017)

Computer Analysis of Images and Patterns , s.95-107 Continue to DOI

Felix Järemo-Lawin, Martin Danelljan, Patrik Tosteberg, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg (2017)

Computer Analysis of Images and Patterns , s.95-107 Continue to DOI

WASP research CVLShow/Hide content