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3DVP_RCNN

Installation

  1. Clone the 3DVP_RCNN repository
# Make sure to clone with --recursive
git clone --recursive https://github.com/yuxng/3DVP_RCNN.git

  1. We'll call the directory that you cloned 3DVP_RCNN into ROOT

    Ignore notes 1 and 2 if you followed step 1 above.

    Note 1: If you didn't clone 3DVP_RCNN with the --recursive flag, then you'll need to manually clone the caffe-fast-rcnn submodule:

    git submodule update --init --recursive

    Note 2: The caffe-fast-rcnn submodule needs to be on the fast-rcnn branch (or equivalent detached state). This will happen automatically if you follow these instructions.

  2. Build the Cython modules

    cd $ROOT/fast-rcnn/lib
make

  3. Build Caffe and pycaffe

    cd $ROOT/caffe-fast-rcnn
# Now follow the Caffe installation instructions here:
#   http://caffe.berkeleyvision.org/installation.html

# If you're experienced with Caffe and have all of the requirements installed
# and your Makefile.config in place, then simply do:
make -j8 && make pycaffe

  4. Download pre-computed 3DVP R-CNN detectors trained on KITTI

    cd $ROOT/fast-rcnn
./data/scripts/fetch_3dvp_rcnn_models.sh

    This will populate the $ROOT/fast-rcnn/data folder with 3DVP_RCNN_models.

Running with the NTHU dataset

  1. The NTHU dataset should have a directory named 'data', under which it has the following structure:

    $data/                           # the directory contains all the data
$data/71                         # a directory for video 71: 000001.jpg, ..., 002956.jpg
$data/71.txt                     # a txt file contains the frame names: 000001 \n 000002 \n ... 002956
# ... and several other directories and txt files ...

  2. Create symlinks for the NTHU dataset

    cd $ROOT/fast-rcnn/data/NTHU
ln -s $data data

  3. Run the region proposal network to generate region proposals, modify the script to run with different videos

    cd $ROOT/fast-rcnn
./experiments/scripts/nthu_caffenet_rpn_6k8k.sh $GPU_ID

  4. Copy the region proposals to $ROOT/fast-rcnn/data/NTHU/region_proposals/RPN_6k8k:

    $ROOT/fast-rcnn/data/NTHU/region_proposals/RPN_6k8k/71    # a directory contains region proposals for video 71: 000001.txt, ..., 002956.txt

  5. Run the detection network, modify the script to run with different videos

    cd $ROOT/fast-rcnn
./experiments/scripts/nthu_caffenet_rcnn_multiscale_6k8k.sh $GPU_ID