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7 papers · 2 filters
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Yaoqing Yang, Chen Feng, Yiru Shen +1
Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and…
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Yiru Shen, Chen Feng, Yaoqing Yang +1
Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieve…
3D Object Discovery and Modeling Using Single RGB-D Images Containing Multiple Object Instances
Wim Abbeloos, Esra Ataer-Cansizoglu, Sergio Caccamo +2
Unsupervised object modeling is important in robotics, especially for handling a large set of objects. We present a method for unsupervised 3D object discovery, reconstruction, and…
Online Convolutional Dictionary Learning for Multimodal Imaging
Kevin Degraux, Ulugbek S. Kamilov, Petros T. Boufounos +1
Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems. In this paper, we propose a new me…
Signal reconstruction via operator guiding
Andrew Knyazev, Alexander Malyshev
Signal reconstruction from a sample using an orthogonal projector onto a guiding subspace is theoretically well justified, but may be difficult to practically implement. We propose…
SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering
Hsiou-Yuan Liu, Dehong Liu, Hassan Mansour +3
Multiple scattering of an electromagnetic wave as it passes through an object is a fundamental problem that limits the performance of current imaging systems. In this paper, we des…