EVAL.md

The parsac.py script can perform both training and evaluation. The --eval flag sets some default options to run evaluation on the test set.

After following the instructions for downloading the datasets and pre-trained network weights (README), you can execute the commands below in order to reproduce the results from our paper.

Main Results

Vanishing Points

SU3

python parsac.py --eval --dataset su3 --data_path datasets/su3 --problem vp --load weights/main_results/vp_su3 --inlier_threshold 0.0001 --instances 8 --hypotheses 32 

YUD

python parsac.py --eval --dataset yud --data_path datasets/yud_plus/data --problem vp --load weights/main_results/vp_su3 --inlier_threshold 0.0001 --instances 8 --hypotheses 32 

NYU-VP

python parsac.py --eval --dataset nyuvp --data_path datasets/nyu_vp/data --problem vp --load weights/main_results/vp_nyu --inlier_threshold 0.0001 --instances 8 --hypotheses 32 

YUD+

python parsac.py --eval --dataset yudplus --data_path datasets/yud_plus/data --problem vp --load weights/main_results/vp_su3 --inlier_threshold 0.0001 --instances 8 --hypotheses 32 

Fundamental Matrices

HOPE-F

python parsac.py --eval --load ./weights/main_results/fundamental --dataset hope --data_path ./datasets/hope --problem fundamental --inlier_threshold 0.01 --assignment_threshold 0.02 --instances 4 --hypotheses 128 

Adelaide

python parsac.py --eval --load ./weights/main_results/fundamental --dataset adelaide --data_path ./datasets/adelaide --problem fundamental --inlier_threshold 0.01 --assignment_threshold 0.02 --instances 4 --hypotheses 128 

Homographies

SMH

python parsac.py --eval --load weights/main_results/homography --dataset smh --data_path datasets/smh --problem homography --inlier_threshold 1e-6 --assignment_threshold 4e-6 --instances 24 --hypotheses 512 

Adelaide

python parsac.py --eval --load weights/main_results/homography --dataset adelaide --data_path datasets/adelaide --problem homography --inlier_threshold 1e-4 --assignment_threshold 4e-3 --instances 24 --hypotheses 512 

Self-Supervised Learning

Weighted Loss

SU3

python parsac.py --eval --dataset su3 --data_path datasets/su3 --problem vp --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --load weights/self_supervised/weighted_su3

NYU-VP

python parsac.py --eval --dataset nyuvp --data_path datasets/nyu_vp/data --problem vp --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --load weights/self_supervised/weighted_nyu

HOPE-F

python parsac.py --eval --load ./weights/self_supervised/weighted_fundamental --dataset hope --data_path ./datasets/hope --problem fundamental --inlier_threshold 0.01 --assignment_threshold 0.02 --instances 4 --hypotheses 128 

Adelaide-F

python parsac.py --eval --load ./weights/self_supervised/weighted_fundamental --dataset adelaide --data_path ./datasets/adelaide --problem fundamental --inlier_threshold 0.01 --assignment_threshold 0.02 --instances 4 --hypotheses 128 

Unweighted Loss

SU3

python parsac.py --eval --dataset su3 --data_path datasets/su3 --problem vp --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --load weights/self_supervised/unweighted_su3

NYU-VP

python parsac.py --eval --dataset nyuvp --data_path datasets/nyu_vp/data --problem vp --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --load weights/self_supervised/unweighted_nyu

HOPE-F

python parsac.py --eval --load ./weights/self_supervised/unweighted_fundamental --dataset hope --data_path ./datasets/hope --problem fundamental --inlier_threshold 0.01 --assignment_threshold 0.02 --instances 4 --hypotheses 128 

Adelaide-F

python parsac.py --eval --load ./weights/self_supervised/unweighted_fundamental --dataset adelaide --data_path ./datasets/adelaide --problem fundamental --inlier_threshold 0.01 --assignment_threshold 0.02 --instances 4 --hypotheses 128 

Ablation Study: Number of Model Instances

python parsac.py --eval --dataset su3 --data_path datasets/su3 --problem vp  --inlier_threshold 0.0001  --hypotheses 32  --load weights/ablation_instances/M_HAT --instances M_HAT

Replace M_HAT with the number of putative model instances. Valid values are {2, 3, 4, 6, 10, 12, 16}.

Ablation Study: Weighted Inlier Counting

with weighted inlier counting

See main results

w/o weighted inlier counting

SU3

python parsac.py --eval --dataset su3 --data_path datasets/su3 --problem vp --load weights/ablation_unweighted/su3 --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --inlier_counting unweighted

HOPE-F

python parsac.py --eval --dataset hope --data_path ./datasets/hope --problem fundamental --load ./weights/main_results/fundamental --inlier_threshold 0.01 --assignment_threshold 0.02 --instances 4 --hypotheses 128  --inlier_counting unweighted

SMH

python parsac.py --eval --load weights/main_results/homography --dataset smh --data_path datasets/smh --problem homography --inlier_threshold 1e-6 --assignment_threshold 4e-6 --instances 24 --hypotheses 512  --inlier_counting unweighted

Ablation Study: Robustness to Noise and Outliers

Note: the following options only work for SU3 and Adelaide.

In order to add Gaussian noise with standard deviation sigma to the input observations, use the following parameter:

--ablation_noise sigma

In order to remove all ground truth outliers from the observations and then add synthetic outliers with an outlier rate of outlier_rate, use the following parameter:

--ablation_outlier_ratio outlier_rate

Ablation Study: Feature Generalisation

We provide line segments extracted with DeepLSD for SU3, NYU-VP and YUD(+). Download and extract the following archive before you can run the ablation study experiments below:
https://cloud.tnt.uni-hannover.de/index.php/s/M7TTyqGzbnCfiJX

Train: LSD / Test: LSD

See main results

Train: LSD / Test: DeepLSD

SU3

python parsac.py --eval --dataset su3 --data_path datasets/su3 --problem vp --load weights/main_results/vp_su3 --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --ablation_deeplsd_folder deeplsd_features/su3

NYU-VP

python parsac.py --eval --dataset nyuvp --data_path datasets/nyu_vp/data --problem vp --load weights/main_results/vp_nyu --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --ablation_deeplsd_folder deeplsd_features/nyu

YUD

python parsac.py --eval --dataset yud --data_path datasets/yud_plus/data --problem vp --load weights/main_results/vp_su3 --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --ablation_deeplsd_folder deeplsd_features/yud

YUD+

python parsac.py --eval --dataset yudplus --data_path datasets/yud_plus/data --problem vp --load weights/main_results/vp_su3 --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --ablation_deeplsd_folder deeplsd_features/yud

Train: DeepLSD / Test: LSD

SU3

python parsac.py --eval --dataset su3 --data_path datasets/su3 --problem vp --load weights/ablation_features/su3_deeplsd/ --inlier_threshold 0.0001 --instances 8 --hypotheses 32  

NYU-VP

python parsac.py --eval --dataset nyuvp --data_path datasets/nyu_vp/data --problem vp --load weights/ablation_features/nyu_deeplsd/ --inlier_threshold 0.0001 --instances 8 --hypotheses 32 

YUD

python parsac.py --eval --dataset yud --data_path datasets/yud_plus/data --problem vp --load weights/ablation_features/su3_deeplsd --inlier_threshold 0.0001 --instances 8 --hypotheses 32 

YUD+

python parsac.py --eval --dataset yudplus --data_path datasets/yud_plus/data --problem vp --load weights/ablation_features/su3_deeplsd --inlier_threshold 0.0001 --instances 8 --hypotheses 32 

Train: DeepLSD / Test: DeepLSD

SU3

python parsac.py --eval --dataset su3 --data_path datasets/su3 --problem vp --load weights/ablation_features/su3_deeplsd/ --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --ablation_deeplsd_folder deeplsd_features/su3

NYU-VP

python parsac.py --eval --dataset nyuvp --data_path datasets/nyu_vp/data --problem vp --load weights/ablation_features/nyu_deeplsd/ --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --ablation_deeplsd_folder deeplsd_features/nyu

YUD

python parsac.py --eval --dataset yud --data_path datasets/yud_plus/data --problem vp --load weights/ablation_features/su3_deeplsd --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --ablation_deeplsd_folder deeplsd_features/yud

YUD+

python parsac.py --eval --dataset yudplus --data_path datasets/yud_plus/data --problem vp --load weights/ablation_features/su3_deeplsd --inlier_threshold 0.0001 --instances 8 --hypotheses 32  --ablation_deeplsd_folder deeplsd_features/yud