@misc{WallravenFlemingCunninghametal., author = {Wallraven, Christian and Fleming, R. W. and Cunningham, Douglas William and Rigau, J. and Feixas, M. and Sbert, M.}, title = {Categorizing art: comparing humans and computers}, language = {en} } @misc{FangTamBorgoetal., author = {Fang, Hui and Tam, Gary K. L. and Borgo, Rita and Aubrey, Andrew and Grant, Philip W. and Rosin, Paul L. and Wallraven, Christian and Cunningham, Douglas William and Marshall, David and Chen, Min}, title = {Visualizing Natural Image Statistics}, series = {IEEE Transactions on Visualization and Computer Graphics}, volume = {19}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {7}, issn = {1941-0506}, pages = {1228 -- 1241}, language = {en} } @misc{JaegerEberhardtCunningham, author = {J{\"a}ger, Matthias and Eberhardt, J{\"o}rg and Cunningham, Douglas W.}, title = {3D reconstruction of partial foot scans using different state of the art neural network approaches}, series = {Footwear Science}, volume = {Volume 16}, journal = {Footwear Science}, number = {2}, publisher = {Taylor\&Francis}, doi = {10.1080/19424280.2024.2319796}, pages = {105 -- 114}, abstract = {As online shopping continues to grow in popularity, shoes are increasingly being purchased without being physically tried on. This has resulted in a significant surge in returns, causing both financial and environmental consequences. To tackle this issue, several systems are available to measure foot dimensions accurately either in-store or at home. By obtaining precise foot measurements, individuals can determine their ideal shoe size and prevent unnecessary returns. In order to make such a system as simple as possible for the user, only a single image should be sufficient to measure the foot. To make this possible, point clouds from one side of the foot, which are generated by taking a depth image, are to be used. Since these point clouds represent only one side of the foot, the other side has to be generated. For this purpose, different existing state of the art networks were tested and compared to determine which architecture is best suited for this task. After implementing, re-training on our own dataset and testing the different architectures, it can be concluded that the point/transormer-based network SnowflakeNet is the most efficient to be used for our task.}, language = {en} }