Pointnet Deep Learning On Point Sets
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Relation-Shape Convolutional Neural Network for Point ...
openaccess.thecvf.comequivariant to point sets learning [24, 27, 48, 19]. Some other works [24, 21] develop alignment network for the robustness to rigid transformation in 3D space. However, the alignment learning is a suboptimal solution for this is-sue. Some traditional descriptors like Fast Point Feature Histograms can be invariant to translation and rotation, yet
SOMA: Solving Optical Marker-Based MoCap Automatically
download.is.tue.mpg.dePointNet methods [7,36] also process the 3D point cloud directly, while learning local features with permutation-invariant pooling operators. Further non-local networks [54] and self-attention-based [52] models can attend glob-ally while learning to focus locally on specific regions of the input. This simple formulation enables learning robust
![Abstract arXiv:1506.02025v3 [cs.CV] 4 Feb 2016](/cache/preview/2/1/c/f/e/5/8/b/thumb-21cfe58bbaf4f01954b3cf2f93929859.jpg)
Abstract arXiv:1506.02025v3 [cs.CV] 4 Feb 2016
arxiv.orgtransformations with neural networks [15, 16, 36], learning and analysing transformation-invariant representations [4, 6, 10, 20, 22, 33], as well as attention and detection mechanisms for feature selection [1, 7, 11, 14, 27, 29]. Early work by Hinton [15] looked at assigning canonical frames of reference to object parts, a theme