Estimation of 3D Information of multiple objects in a monocular Image

Abstract

This paper proposes a method for estimating 3D information, such as shape, orientation, size, and position of objects in a monocular image, and reproduce scenes in 3D point clouds using Convolutional Neural Network (CNN). This study proposes a network that combines depth estimation, object detection, and point cloud estimation to estimate 3D information of objects. The proposed network requires networks for object detection and segmentation, and a point cloud estimation for object shape estimation. The point cloud estimation network is robust to the reproduction of the object's surface and can deal with unknown objects through a semantic understanding of the object’s shape. In addition to these networks, we combine a depth estimation network for estimating the depth of the entire scene and the distance between the camera and object. In this paper, we consider the point cloud estimation network. We estimate the point clouds for real objects in the images of the dataset and evaluate the output point clouds.