The release code and dataset of CNN-MonoFusion for ismar2018.
The project contain two submodules (depth-esti/pointcloud-fusion).
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Network-Archi can be found at adenet_def.py. We name our network as adenet (adaptive-depth-estimation-network), which combined the resnet50/astrous/concat layers and trained using our adaptive-berhu loss.
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For online depth prediction, you need run adenet_run_as_server.py in your server.
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The pointcloud-fusion is used for fusion stage. We build our system depended on a mono-slam system, so you need to incorpolate our fusion code into a slam sysytem like ORB-SLAM.
We run our whole system in Win7&Win10 locally ok.
You can also running the network on the server compute for accerlating the speed!
- Tensorflow >= 1.4.0 & CUDA>=8.0
- Python 3.5.2
- CMake 3.6.0
- Visual Studio 2015
- Optional: kinect-v2 for collecting your own dataset
All the images are collected by NetEaseAI-CVLab.
Copyright @2018 CNN-MonoFusion Authors. All rights reserved.
Please download the NEAIR-dataset here.
You can download our models used in the paper here
If you find CNN_MonoFusion useful in your research, please consider citing:
@article{
Author = {Jiafang Wang, Haiwei Liu, Lin Cong, Zuoxin Xiahou, and Liming Wang},
Title = {CNN-MonoFusion: Online Monocular Dense Reconstruction using Learned Depth from Single View},
Journal = {},
Year = {}
}