%0 Generic %1 li2018pointcnn %A Li, Yangyan %A Bu, Rui %A Sun, Mingchao %A Chen, Baoquan %D 2018 %K 2018 arxiv cnn deep-learning point-cloud %T PointCNN %U http://arxiv.org/abs/1801.07791 %X We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point cloud are irregular and unordered, thus a direct convolving of kernels against the features associated with the points will result in deserting the shape information while being variant to the orders. To address these problems, we propose to learn a X-transformation from the input points, and then use it to simultaneously weight the input features associated with the points and permute them into latent potentially canonical order, before the element-wise product and sum operations are applied. The proposed method is a generalization of typical CNNs into learning features from point cloud, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.

@misc{li2018pointcnn, abstract = {We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point cloud are irregular and unordered, thus a direct convolving of kernels against the features associated with the points will result in deserting the shape information while being variant to the orders. To address these problems, we propose to learn a X-transformation from the input points, and then use it to simultaneously weight the input features associated with the points and permute them into latent potentially canonical order, before the element-wise product and sum operations are applied. The proposed method is a generalization of typical CNNs into learning features from point cloud, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.}, added-at = {2018-07-20T09:21:48.000+0200}, author = {Li, Yangyan and Bu, Rui and Sun, Mingchao and Chen, Baoquan}, biburl = {https://www.bibsonomy.org/bibtex/2a18e59b707a098dcb0bfc12894c3e137/analyst}, description = {[1801.07791] PointCNN}, interhash = {52048af3e658b0e985c0fa13a688f87e}, intrahash = {a18e59b707a098dcb0bfc12894c3e137}, keywords = {2018 arxiv cnn deep-learning point-cloud}, note = {cite arxiv:1801.07791Comment: Small updates}, timestamp = {2018-07-20T09:21:48.000+0200}, title = {PointCNN}, url = {http://arxiv.org/abs/1801.07791}, year = 2018 }