This code has been developed under Anaconda(Python 3.6), Pytorch 1.6.0, Torchvision 0.7.0 and CUDA 10.1.
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Please install following environments:
# create a new environment if needed conda create --name stad conda activate stad # install the the dependencies pip install -r requirements.txt
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Download KITTI data
(1) Download raw data
- Option1. Follow the instruction from BTS github
- Option2. Download the raw dataset from KITTI dataset page
(2) Download ground truth data
- Download official ground truth from KITTI dataset page
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Download pretrained weights
- From google drive
In the code folder, run
sh local_train_kitti.sh
You need to change dataset_path
, exp_name
and loss_type
correctly.
Please set parameters as follows:
Models | exp_name | loss_type |
---|---|---|
Neural-RGBD | ver0-nrgbd | NLL |
Neural-RGBD with scale-invariant loss | ver0-nrgbd_silog | silog |
STAD-frame (Ours) | ver1-per_frame_silog | silog |
STAD (Ours) | ver4-aggr_silog | silog |
In this case we assume the camera poses are given with the dataset.
In the code folder, run
sh local_test.sh
You need to change dataset_path
and model_path
correctly.
If you have any questions, please contact the author Hyunmin Lee<hyunmin057@gmail.com>.
Portions of the source code (e.g., training pipeline, argument parser, and logger) are from NVIDIA, Neural-RGBD