implement some algorithms of 6d pose estimation
- linemod
- linemod levelup
- linemod ros
- latent class hough tree(LCHF)
- 3d convex segmentation
note: LCHF is too hard to tune params so I'm not going to implement it...
a better choice: patch_linemod
files: params/ pysixd/ t_less_toolkit/ tools/
copied from sixd_toolkit
deal with model reading/rendering, datasets reading and evaluation
get dataset under 6DPose folder using following cmd
wget -r -np -nH --cut-dirs=1 -R index.html http://ptak.felk.cvut.cz/6DB/public/
install opencv3 with contrib rgbd module
install pybind11
pip3 install -r requirements.txt
in target folder:
mkdir build
cd build/
cmake ..
make
in 6dpose folder, if use pybind:
pip3 install target_folder/
python3 target.py
Codes in linemod.py will train and detect objects in downloaded dataset.
Refer to opencv linemod and ork linemod src
Linemod suffers a lot from scale and occlusion problems, to deal wtih them:
- set low response closer to zero;(idea got from focal loss)
- use depth histgram and 1D nms to find some primary scales
ori linemod can't run while our features are more than 64, due to 8bit SSE. after changing to 16bit SSE and preventing overflow, we can have up to 8192 features now.
run linemod with ros for real test, not done yet
- linemod patch version
- hough forest
- meanshift for leaf mode
- icp for pose refine
- train and test
segment 3d cloud to convex part, core func have been done, try to deploy now
NOTE: To use pybind, static super4pcs must be built with flag -fPIC. Build super4pcs using following lines:
mkdir build
cd build
cmake .. -DCMAKE_CXX_FLAGS="-fpic"
make
sudo make install