Survey of 3D Model Recognition Based on Deep Learning

Abstract

With the rapid advancement of three-dimensional visual perception devices such as 3D scanners and LiDAR, the field of 3D model recognition is gradually gaining the attention of a growing number of researchers. This domain encompasses two core tasks: 3D model classification and retrieval. Since deep learning has already achieved significant success in two-dimensional visual tasks, its introduction into the realm of three-dimensional visual perception not only breaks free from the constraints of traditional methods but also makes notable strides in areas such as autonomous driving and intelligent robotics. However, the application of deep learning techniques to 3D model recognition tasks still faces several challenges. In light of this, there is a need for a comprehensive review of the application of deep learning in 3D model recognition. This review begins by discussing commonly used evaluation metrics and public datasets, providing relevant information and sources for each dataset. Subsequently, it delves into representative methods from various angles, including point clouds, views, voxels, and multimodal fusion. It also summarizes recent research development in the field. Through performance comparison on these datasets, the strengths and limitations of each method are analyzed. Finally, based on the merits and demerits of these approaches, the review outlines the challenges currently faced by 3D model recognition tasks and provides an outlook on future trends in this field.

Keywords

• three-dimensional vision; deep learning; point clouds; views; voxels; multimodal