Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

Most prior work represents the shapes of point clouds by coordinates. However, it is
insufficient to describe the local geometry directly. In this paper, we present RepSurf …

Discrete point cloud objects lack sufficient shape descriptors of 3D geometries. In this paper,
we present a novel method for aggregating hypothetical curves in point clouds. Sequences …

Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to
capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural …

Abstract 3D shape recognition has attracted much attention recently. Its recent advances
advocate the usage of deep features and achieve the state-of-the-art performance. However …

Point cloud processing is very challenging, as the diverse shapes formed by irregular points
are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently …

The performance of object detection has recently been significantly improved due to the
powerful features learnt through convolutional neural networks (CNNs). Despite the …

Most existing 3D object recognition algorithms focus on leveraging the strong discriminative
power of deep learning models with softmax loss for the classification of 3D data, while …

Hyperspectral image (HSI) classification is an active and important research task driven by
many practical applications. To leverage deep learning models especially convolutional …

Learning 3D global features by aggregating multiple views is important. Pooling is widely
used to aggregate views in deep learning models. However, pooling disregards a lot of …