  The success of supervised learning requires large-scale ground truth labels
which are very expensive, time-consuming, or may need special skills to
annotate. To address this issue, many self- or un-supervised methods are
developed. Unlike most existing self-supervised methods to learn only 2D image
features or only 3D point cloud features, this paper presents a novel and
effective self-supervised learning approach to jointly learn both 2D image
features and 3D point cloud features by exploiting cross-modality and
cross-view correspondences without using any human annotated labels.
Specifically, 2D image features of rendered images from different views are
extracted by a 2D convolutional neural network, and 3D point cloud features are
extracted by a graph convolution neural network. Two types of features are fed
into a two-layer fully connected neural network to estimate the cross-modality
correspondence. The three networks are jointly trained (i.e. cross-modality) by
verifying whether two sampled data of different modalities belong to the same
object, meanwhile, the 2D convolutional neural network is additionally
optimized through minimizing intra-object distance while maximizing
inter-object distance of rendered images in different views (i.e. cross-view).
The effectiveness of the learned 2D and 3D features is evaluated by
transferring them on five different tasks including multi-view 2D shape
recognition, 3D shape recognition, multi-view 2D shape retrieval, 3D shape
retrieval, and 3D part-segmentation. Extensive evaluations on all the five
different tasks across different datasets demonstrate strong generalization and
effectiveness of the learned 2D and 3D features by the proposed self-supervised
method.
