Mingchao Sun

TL;DR: This work proposes to learn an Χ-transformation from the input points to simultaneously promote two causes: the first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order.

TL;DR: This paper shows with extensive experiments that the mere replacement of conventional convolutional layers with DO-Conv layers boosts the performance of CNNs on many classical vision tasks, such as image classification, detection, and segmentation.

TL;DR: The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus it is called PointCNN, and experiments show that it achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.

TL;DR: A large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database and the best performing teams have outperformed state-of-the-art approaches on both tasks.

TL;DR: This work proposes a new approach of enlarging the normal neighborhood in the aggregation of GNNs, which aims at maximizing mutual information and improves the state-of-the-art performance for four types of graph tasks, including supervised and semi-supervised graph classification, graph link prediction and graph edge generation and classification.