35 citations · 96 across the 20 of their papers we have counts for
9 papers · 1 filter
Spectral-GANs for High-Resolution 3D Point-cloud Generation
Sameera Ramasinghe, Salman Khan, Nick Barnes +1
Point-clouds are a popular choice for vision and graphics tasks due to their accurate shape description and direct acquisition from range-scanners. This demands the ability to synt…
Representation Learning on Unit Ball with 3D Roto-Translational Equivariance
Sameera Ramasinghe, Salman Khan, Nick Barnes +1
Convolution is an integral operation that defines how the shape of one function is modified by another function. This powerful concept forms the basis of hierarchical feature learn…
Geometric Back-projection Network for Point Cloud Classification
Shi Qiu, Saeed Anwar, Nick Barnes
As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network that ca…
Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes
Sameera Ramasinghe, Salman Khan, Nick Barnes +1
Existing networks directly learn feature representations on 3D point clouds for shape analysis. We argue that 3D point clouds are highly redundant and hold irregular (permutation-i…
Densely Residual Laplacian Super-Resolution
Saeed Anwar, Nick Barnes
Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architect…
Unsupervised Primitive Discovery for Improved 3D Generative Modeling
Salman H. Khan, Yulan Guo, Munawar Hayat +1
3D shape generation is a challenging problem due to the high-dimensional output space and complex part configurations of real-world objects. As a result, existing algorithms experi…