Model-evaluator of Publication : "SMSnet: Semantic Motion Segmentation using Deep Convolutional Neural Networks"
The models can be downloaded here: Models This link contains the models trained on City-Kitti-Motion using EFS and without EFS on inf range and 40m range
The databases can be downloaded here:
Each compressed archiv includes 8 lmdb databases:
Labels:
- IMAGES_LMDB0: Labels with 20m range
- IMAGES_LMDB1: Labels with 40m range
- IMAGES_LMDB2: Labels with 60m range
- LABELS_LMDB: Labels with inf range
Images:
- IMAGES_LMDB3: Left image corresponding to label
- IMAGES_LMDB4: Previous left image
Flow: Note: The flow is centered at 128 and scaled by 1/6.4 in order to fit into the UC1 LMDB format
- IMAGES_LMDB5: Flow with EFS
- IMAGES_LMDB6: Flow
The full moving car/ static car annotations for the training and validation sets can be downloaded here: Annotations
- Caffe
- CUDA
- OPENCV
- HDF5
- PROTOBUF
This repo comes with a modified version of caffe
- Clone repo
- Go into extern/modcaffe
- Create a build folder "mkdir build && cd build"
- Compile: "cmake .." then "make -j8"
- Go to repo and create build dir: "mkdir build && mkdir datasets && mkdir models"
- Download KITTI dataset from the provided link and extract it into "datasets"
- Download Models and extract them into "models"
- Go to build folder: "cd build"
- Compile: "cmake .." then "make"
- run program : "./caffe_test_ex" . The network and the KITTI database should load. The network predictions and the GT is visualized. To get the next prediction just press Enter. If you want to change the Dataset take a look in the code (main.cpp)
If you get : "caffe/proto/caffe.pb.h: No such file or directory" try to go in your caffe dir (extern/modcaffe) and type:
- protoc src/caffe/proto/caffe.proto --cpp_out=.
- mkdir include/caffe/proto
- mv src/caffe/proto/caffe.pb.h include/caffe/proto
If you use this code or the datasets please make sure that you cite the following papers:
@InProceedings{Vertens17Icra,
author = {Johan Vertens, Abhinav Valada and Wolfram Burgard},
title = {SMSnet: Semantic Motion Segmentation
using Deep Convolutional Neural Networks},
year = 2017,
month = sept,
url = {https://github.com/JohanVer/SMSnet},
address = {Vancouver, Canada}
}
@article{DBLP:journals/corr/CordtsORREBFRS16,
author = {Marius Cordts and
Mohamed Omran and
Sebastian Ramos and
Timo Rehfeld and
Markus Enzweiler and
Rodrigo Benenson and
Uwe Franke and
Stefan Roth and
Bernt Schiele},
title = {The Cityscapes Dataset for Semantic Urban Scene Understanding},
journal = {CoRR},
volume = {abs/1604.01685},
year = {2016},
url = {http://arxiv.org/abs/1604.01685},
timestamp = {Wed, 07 Jun 2017 14:41:02 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/CordtsORREBFRS16},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@ARTICLE{Geiger2013IJRR,
author = {Andreas Geiger and Philip Lenz and Christoph Stiller and Raquel Urtasun},
title = {Vision meets Robotics: The KITTI Dataset},
journal = {International Journal of Robotics Research (IJRR)},
year = {2013}
}