Monocular Depth Estimation using Synthetic Data for an Augmented Reality Training System in Laparoscopic Surgery

Author:

Schreiber Andre M.,Hong Minsik,Rozenblit Jerzy W.

Funder

National Science Foundation

Publisher

IEEE

Reference29 articles.

1. Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue;garg;European Conference on Computer Vision,2016

2. Single-Image Depth Perception in the Wild;chen;Proceedings of the 30th International Conference on Neural Information Processing Systems,2016

3. Training Deep Networks with Synthetic Data?: Bridging the Reality Gap by Domain Randomization;tremblay;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops,2018

4. Understanding Real World Indoor Scenes With Synthetic Data;handa;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016

5. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation

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1. Monocular depth estimation via cross-spectral stereo information fusion;Multimedia Tools and Applications;2024-01-04

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