yheno's repositories
Mask_RCNN
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
SurfaceNet
M. Ji, J. Gall, H. Zheng, Y. Liu, and L. Fang. SurfaceNet: An End-to-end 3D Neural Network for Multiview Stereopsis. ICCV, 2017
crl
Implementation of the paper "Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching"
mx-maskrcnn
A MXNet implementation of Mask R-CNN
sgm
Semi-Global Matching on the GPU
apollo
An open autonomous driving platform
DA-RNN
Semantic Mapping with Data Associated Recurrent Neural Networks
ros_comm
ROS communications-related packages, including core client libraries (roscpp, rospy, roslisp) and graph introspection tools (rostopic, rosnode, rosservice, rosparam).
FRRN
Full Resolution Residual Networks for Semantic Image Segmentation
flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
matplotlib-cpp
Extremely simple yet powerful header-only C++ plotting library built on the popular matplotlib
models
Models built with TensorFlow
VINS-Mobile
Monocular Visual-Inertial State Estimator on Mobile Phones
mrpt
:zap: The Mobile Robot Programming Toolkit (MRPT)
charls
CharLS, a C++ JPEG-LS library implementation
pykitti
Python tools for working with KITTI data.
okvis
OKVIS: Open Keyframe-based Visual-Inertial SLAM.
lua
The Lua programming language with CMake based build
KittiSeg
A Kitti Road Segmentation model implemented in tensorflow.
mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
snapnet
SnapNet for Semantic3D dataset
tensorflow-vgg
VGG19 and VGG16 on Tensorflow
PSPNet
Repository for paper https://arxiv.org/abs/1612.01105
tensorflow-fcn
An Implementation of Fully Convolutional Networks in Tensorflow.
pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
osu
rhythm is just a *click* away!
resmatch
Implementation of "Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning"