Analyzing the geometric and semantic properties of 3D point clouds through the deep networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. This paper presents a new method to define and compute convolution directly on 3D point clouds by the proposed annular convolution. This new convolution operator can better capture the local neighborhood geometry of each point by specifying the (regular and dilated) ring-shaped structures and directions in the computation. It can adapt to the geometric variability and scalability at the signal processing level. We apply it to the developed hierarchical neural networks for object classification, part segmentation, and semantic segmentation in large-scale scenes. The extensive experiments and comparisons demonstrate that our approach outperforms the state-of-the-art methods on a variety of standard benchmark datasets (e.g., ModelNet10, ModelNet40, ShapeNet-part, S3DIS, and ScanNet).

Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large labeled training dataset requires human involvement, which is expensive and time consuming. Semi-supervised methods can alleviate this problem by utilizing a limited number of labeled data in conjunction with sufficiently large unlabeled data to construct a classification model. Self-training techniques are among the earliest semi-supervised methods proposed to enhance learning by utilizing unlabeled data. In this paper, we propose a deep semi-supervised learning (DSSL) self-training method that utilizes the strengths of both supervised and unsupervised learning within a single model. We measure the efficacy of the proposed method on semi-supervised visual object classification tasks using the datasets CIFAR-10, CIFAR-100, STL-10, MNIST, and SVHN. The experiments show that DSSL surpasses semi-supervised state-of-the-art methods for most of the aforementioned datasets.