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

07/31/2016
by   Balint Antal, et al.
0

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.

READ FULL TEXT

page 2

page 3

page 4

research
05/18/2019

Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural Network

There has been tremendous research progress in estimating the depth of a...
research
09/18/2022

StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks

Obstacle detection is a safety-critical problem in robot navigation, whe...
research
03/30/2020

From Patterson Maps to Atomic Coordinates: Training a Deep Neural Network to Solve the Phase Problem for a Simplified Case

This work demonstrates that, for a simple case of 10 randomly positioned...
research
06/14/2022

Reconstructing vehicles from orthographic drawings using deep neural networks

This paper explores the current state-of-the-art of object reconstructio...
research
07/10/2017

Feature Joint-State Posterior Estimation in Factorial Speech Processing Models using Deep Neural Networks

This paper proposes a new method for calculating joint-state posteriors ...
research
03/30/2023

Implicit View-Time Interpolation of Stereo Videos using Multi-Plane Disparities and Non-Uniform Coordinates

In this paper, we propose an approach for view-time interpolation of ste...
research
12/02/2017

Fruit recognition from images using deep learning

In this paper we introduce a new, high-quality, dataset of images contai...

Please sign up or login with your details

Forgot password? Click here to reset