SoK: Vehicle Orientation Representations for Deep Rotation Estimation

Raymond H. Tu, Siyuan Peng, Valdimir Leung, Richard Gao, Jerry Lan

This is the official implementation for the paper SoK: Vehicle Orientation Representations for Deep Rotation Estimation

Table of Conents

• Environment Setup
• Training
• Training Result

Envrionment Setup

Install required packages via conda

# create conda environment based on yml file
conda env update --file environment.yml
# activate conda environment
conda activate KITTI-Orientation

Clone git repo:

git clone [email protected]:umd-fire-coml/KITTI-orientation-learning.git

Training

Check training.sh for example training script

Training Parameter setup:

Training parameters can be configured using cmd arguments

• --predict: Specify prediction target. Options are rot-y, alpha
• --converter: Specify prediction method. Options are alpha, rot-y, tricosine, multibin, voting-bin, single-bin
• --kitti_dir: path to kitti dataset directory. Its subdirectory should have training/ and testing/ Default path is dataset/
• --training_record: root directory of all training record, parent of weights and logs directory. Default path is training_record
• --resume: Resume from previous training under training_record directory
• --add_pos_enc: Add positional encoding to input
• --add_depth_map: Add depth map information to input

For all the training parameter setup, please using

python3 model/training.py -h

Training Result