Recognition of Point Sets Objects in Indoor Scenes

EasyChair Preprint 843

Abstract

With the wide application of 3D intelligent sensing technologies  in robotics and driverless driving field. Most researchers will transform point cloud data to regular 3D voxel grids, collections of images, depth maps, etc. which will inevitably lead to huge data processing problems. In this paper, we consider the problem of recognizing objects in indoor senses. We first use Euclidean distance clustering method to segment objects in indoor scenes. Then we use a deep learning network structure to directly extract features of the point cloud data to recognize the objects. Theoretically, this network structure shows strong performance. In experiment, there is an accuracy rate of 89.7\% on the test set, this method is superior to current mainstream methods. Experiments show that the proposed network structure can accurately identify objects in indoor scenes and has strong robustness.

Keyphrases: 3D model recognition, deep learning, point cloud

@booklet{EasyChair:843,
  author    = {Ruizhen Gao and Xiaohui Li and Jingjun Zhang},
  title     = {Recognition of Point Sets Objects in Indoor Scenes},
  howpublished = {EasyChair Preprint 843},
  year      = {EasyChair, 2019}}