Regularizing Self-supervised 3D Scene Flows with Surface Awareness and Cyclic Consistency

Installation

• Install Fast Geodis with pip install FastGeodis --no-build-isolation
• Install PyTorch3d with CUDA support.

• Run commands in install.sh for installation of the packages above

DATA

• Setup directory for extracting the data, visuals and experimental results

export BASE_PATH='path_where_to_store_data'

• Download Data and unpack it to the folder $BASE_PATH/data/sceneflow:

tar -xvf data_sceneflow.tgz $BASE_PATH/data/sceneflow

Run Experiments

To run the method on all datasets with final metrics printed on cuda:0, just type:

for i in {0..6}; do python evaluate_flow.py $i; done

where the argument sets the specific datasets according to the following table:

Dataset	Argument Number	Model
KITTI t	0	Neural Prior
StereoKITTI	1	Neural Prior
KITTI t	2	SCOOP
StereoKITTI	3	SCOOP
Argoverse	4	Neural Prior
Nuscenes	5	Neural Prior
Waymo	6	Neural Prior

Experimental results on LiDAR Datasets

Experimental results on StereoKITTI Dataset

Qualitative Example