3D Point Cloud Generation to Understand Real Object Structure via Graph Convolutional Networks

Estimating and generating a three-dimensional (3D) model from a single image are challenging problems that have gained considerable attention from researchers in different fields of computer vision and artificial intelligence. Previously, there has been research work on single-angle and multi-view object use for 3D reconstruction. 3D data can be represented in many forms like meshes, voxels, and point clouds. This article presents 3D reconstruction using standard and state-of-the-art methods. Conventionally, to estimate the 3D many systems investigate multi-view images, stereo images, or object scanning with the support of additional sensors like Light Detection and Ranging (LiDAR) and depth sensors. The proposed semi-neural network system is the blend of neural network and also image processing filters and machine learning algorithms to extract features that have been used in the network. Three different types of features have been used in this paper that will help to estimate the 3D of the object from a single image. These features include semantic segmentation, depth of image, and surface normal. Semantic segmentation features have been extracted from the segmentation filter that has been exploited for extracting the object portion. Similarly, depth features have been used to estimate the object in the z-axis from NYUv2 dataset training using SENET-154 architecture. Finally surface normal features have been extracted based on estimated depth results using edge detection, and horizontal and vertical convolutional filters. Surface normal helps in determining the x, y and, z orientations of an object. The final representation of the object model has been in the form of a 3D point cloud. The resultant 3D point cloud has made it easy to analyze the model quality by points and distance representing intermodal and ground truth. In this article, three publicly available benchmark datasets have been used for system evaluation and experimental assessment including ShapeNetCore, ModelNet10 and ObjectNet3D datasets. The ShapeNetCore has archived an accuracy of 95.41% and chamfer distance of 0.00098, the ModelNet10 dataset has achieved an accuracy of 94.74% and chamfer distance of 0.00132 and finally, the ObjectNet3D dataset has achieved an accuracy of 95.53% and chamfer distance 0.00091. The results of many classes of the proposed system are outstanding at visualization as compared to standard methods.