whoiszzj
commented
2 months ago Some of the parameters and method calls in our provided code are tailored for ETH3D. If you want to reproduce our results on the Tanks & Temples dataset, appropriate modifications are required. The main changes are as follows:
In the main.cpp, modify the ComputeRoundNum function, changing max_size > 1000 to max_size > 800 so that the algorithm runs for three rounds.
For the Intermediate dataset in T&T, use the RunFusion_TAT_Intermediate method from the code, and for the Advanced dataset in T&T, use the RunFusion_TAT_Advanced method (both methods are in the APD.cpp file). Since these two sub-datasets differ significantly from the ETH3D dataset, we made some appropriate adjustments based on the fusion methods used in deep learning approaches.
These should be the main differences. Also, when running T&T, use a short camera and set the depth interval to 256.
Thanks for your excellent work. I tested the project on both ETH3D dataset and tanks & temple dataset, I get similar result in ETH3D dataset, but I have tested the tanks & temple dataset for twice, the results are worse than the paper illustrated. I used the dataset as the link the APD-MVS project provided. I wonder whether there is anything wrong with this results? The result I get: F-score: Auditorium: 21.45, Ballroom: 35.46, Courtroom: 35.85, Family: 72.37, rancis: 58.69 , Horse: 46.06, Lighthouse: 64.26, M60: 57.25 , Museum: 48.11, Palace: 24.85, Panther: 57.82, Playground: 59.52, Temple: 36.19, Train: 49.12, Mean intermediate: 58.14 Mean advanced: 33.65
The result in the paper: Mean intermediate: 63.64 Mean advanced: 39.91