SparseVoxNet: 3-D Object Recognition With Sparsely Aggregation of 3-D Dense Blocks

Automatic recognition of 3-D objects in a 3-D model by convolutional neural network (CNN) methods has been successfully applied to various tasks, e.g., robotics and augmented reality. Three-dimensional object recognition is mainly performed by analyzing the object using multi-view images, depth images, graphs, or volumetric data. In some cases, using volumetric data provides the most promising results. However, existing recognition techniques on volumetric data have many drawbacks, such as losing object details on converting points to voxels and the large size of the input volume data that leads to substantial 3-D CNNs. Using point clouds could also provide very promising results; however, point-cloud-based methods typically need sparse data entry and time-consuming training stages. Thus, using volumetric could be a more efficient and flexible recognizer for our special case in the School of Medicine, Shanghai Jiao Tong University. In this article, we propose a novel solution to 3-D object recognition from volumetric data using a combination of three compact CNN models, low-cost SparseNet, and feature representation technique. We achieve an optimized network by estimating extra geometrical information comprising the surface normal and curvature into two separated neural networks. These two models provide supplementary information to each voxel data that consequently improve the results. The primary network model takes advantage of all the predicted features and uses these features in Random Forest (RF) for recognition purposes. Our method outperforms other methods in training speed in our experiments and provides an accurate result as good as the state-of-the-art.

I. Introduction

With the rapid development of 3-D reconstruction and modeling techniques, the 3-D model’s repositories have become very large. These repositories may include a variety of 3-D models that requires categorization. However, this is an arduous task to do manually, and thus, having automatic 3-D recognition methods is necessary. There exists a large number of methods on 3-D object recognition based on the use of point-clouds [1]–[4], volumetric information [5]–[8], or even multi-view images [9]–[12]. Among them, using volumetric data, similar to the pixels in a bitmap that naturally encode the spatial distribution of 3-D shapes, has shown very promising results.