Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks

TL;DR

This paper introduces a deep neural network-based method for automatically reconstructing 3D point clouds from stereo laparoscopic images, demonstrating promising results on a public dataset.

Contribution

It presents a novel deep learning approach that directly maps stereo image pixel intensities to 3D coordinates for surgical applications.

Findings

Effective 3D reconstruction from stereo images

Promising accuracy on public dataset

Detailed neural network architecture analysis

Abstract

In this paper, an automatic approach to predict 3D coordinates from stereo laparoscopic images is presented. The approach maps a vector of pixel intensities to 3D coordinates through training a six layer deep neural network. The architectural aspects of the approach is presented and in detail and the method is evaluated on a publicly available dataset with promising results.