Pinned Repositories
cs231n_pytorch
csvworker
Small C++ library for working with csv files.
DeepPruner_SCARED
MICCAI 2019 Endovis SCARED challenge, DeepPrunner submision
HAPNet-Hierarchically-aggregated-pyramid-network-for-real-time-stereo-matching
HAPNet is a deep learning architecture which avoids the explicit construction of a cost volume of similarity which is one of the most computationally costly blocks of stereo algorithms. This makes training our network significantly more efficient and avoids the needs for large memory allocation. Our method performs well, especially around regions compromising multiple discontinuities around surgical instrumentation or around complex small structures and instruments. The method compares well to the state-of-the-art techniques while taking a different methodological angle to computational stereo problem in surgical video.
high-res-stereo
Hierarchical Deep Stereo Matching on High Resolution Images, CVPR 2019.
msdesis
MSDESIS: Multi-task stereo disparity estimation and surgical instrument segmentation
opencv_360video
ris2017_toolkit
Unofficial MICCAI 2017 Robotic Instrument Segmentation toolkit
scared_toolkit
Unofficial SCARED Dataset Toolkit
u_cv_python
implementation of CV algorithms
dimitrisPs's Repositories
dimitrisPs/scared_toolkit
Unofficial SCARED Dataset Toolkit
dimitrisPs/msdesis
MSDESIS: Multi-task stereo disparity estimation and surgical instrument segmentation
dimitrisPs/DeepPruner_SCARED
MICCAI 2019 Endovis SCARED challenge, DeepPrunner submision
dimitrisPs/opencv_360video
dimitrisPs/ris2017_toolkit
Unofficial MICCAI 2017 Robotic Instrument Segmentation toolkit
dimitrisPs/u_cv_python
implementation of CV algorithms
dimitrisPs/cs231n_pytorch
dimitrisPs/csvworker
Small C++ library for working with csv files.
dimitrisPs/HAPNet-Hierarchically-aggregated-pyramid-network-for-real-time-stereo-matching
HAPNet is a deep learning architecture which avoids the explicit construction of a cost volume of similarity which is one of the most computationally costly blocks of stereo algorithms. This makes training our network significantly more efficient and avoids the needs for large memory allocation. Our method performs well, especially around regions compromising multiple discontinuities around surgical instrumentation or around complex small structures and instruments. The method compares well to the state-of-the-art techniques while taking a different methodological angle to computational stereo problem in surgical video.
dimitrisPs/high-res-stereo
Hierarchical Deep Stereo Matching on High Resolution Images, CVPR 2019.
dimitrisPs/klipper
Klipper is a 3d-printer firmware
dimitrisPs/opencv_contrib
Repository for OpenCV's extra modules
dimitrisPs/RAFT-Stereo
dimitrisPs/ros_LAR_robot
dimitrisPs/stm32