30 citations · 91 across the 44 of their papers we have counts for
4 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…
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…
Volumetric Convolution: Automatic Representation Learning in Unit Ball
Sameera Ramasinghe, Salman Khan, Nick Barnes
Convolution is an efficient technique to obtain abstract feature representations using hierarchical layers in deep networks. Although performing convolution in Euclidean geometries…