Hi, in the paper Planar Prior Assisted PatchMatch Multi-View Stereo you say maybe you will combine ACMP with multi-scale strategy in the future, is the algorithm ACMMP the implementation? It looks like so from the name.

If you do not know about ACMMP, do you implement ACMP + ACMM yourself? Will it be much better than ACMP? And how about the speed?

Thank you for making your code available!

I am trying to use your ACMP code to reproduce your reconstruction results that are posted on the ETH3D website and that were published in your AAAI-20 paper. My methodology is:

1. Build the ACMP code from this repository (including PR #4)
2. Download the High-res multi-view training datasets (multi_view_training_dslr_undistorted.7z) from the ETH3D website
3. Use colmap2mvsnet_acm.py with default parameters to convert the COLMAP files to MVSNet format
4. Run the ACMP executable to reconstruct 3D point clouds
5. Run ETH3DMultiViewEvaluation to compute the F₁ score at 2 cm tolerance

When I use your code to reconstruct and evaluate the point clouds in this manner, I get some F₁ scores that differ significantly from those posted on the ETH3D website:

| Dataset | F₁ score
(GitHub
code)| F₁ score
(ETH3D
website) | Δ | | ---------- | -------- | -------- | -------- | | courtyard | 84.09 | 85.61 | -1.52 | | delivery | 85.51 | 85.68 | -0.17 | | electro | 86.23 | 85.61 | +0.62 | | facade | 69.06 | 69.59 | -0.53 | | kicker | 72.83 | 77.33 | -4.50 | | meadow | 60.53 | 71.00 | -10.47 | | office | 74.17 | 74.45 | -0.28 | | pipes | 68.95 | 69.16 | -0.21 | | playground | 69.23 | 71.88 | -2.65 | | relief | 84.74 | 83.15 | +1.59 | | relief_2 | 84.21 | 83.37 | +0.84 | | terrace | 90.19 | 89.92 | +0.27 | | terrains | 88.67 | 90.57 | -1.90 |

I realize that the algorithm is non-deterministic, and that F₁ scores may vary slightly from one execution to the next. However, the F₁ scores for the kicker and meadow datasets are much worse when I run this ACMP code, compared to the results posted on the ETH3D website.

What are the differences between the code posted in this repository and the code that was used to generate the results in the paper and posted on the ETH3D website?

Hi I would like to thank the author who did this awesome research. I can get very good results. Is there a way to speed up these patchmatch and fusion? Any reference or advice would be highly appreciated. Thank you

Hey!

Are there any memory requirements for this codebase? I'm working with (~4000) 918x1368 images and often find the codebase exists with "Killed" indicating that I am running out of memory (128G) at some random point during the geometric consistency phase. Is this related to #4?

• fix default permissions (777 is a bad idea as it's way too much permissive)
• correct format for size_t
• fix an out of array bounds access
• fix leaks as cv::Mat does not take ownership of data pointer and thus cannot de allocate the underlying memory.

same fixes hold for ACMM and ACMH. I'll make a dedicated PR for the two other repos as well if you are ok with that.

https://arxiv.org/pdf/1912.11744.pdf

In your Graphical model of view selection, when you define the probability of having Xij given Zj and Theta_i, it seems to be independent from Zj. Since mij corresponds to the cost of a given Theta hypothesis

In this case the view selection probability (Eq 11) is reduced to being proportional to P(Zj | Znj)

I thus also don't get your Monte-Carlo sampling to define the weight for each source image Ij as wj.

What am I missing ?

Many thanks

Thanks for your great work! I get excellent result on my indoor dataset. But when I try to visualize the depth map, I find it's ugly compared with colmap depth map. I just normalize the depth map through (depthmap - depthmap.min)/(depthmap.max - depthmap.min). Is there anything better I can do? colmap depth map: ACMP depth map: Planar model:

Hi, I find a confusing code here, is this an unused code or a bug? https://github.com/GhiXu/ACMP/blob/604e2347f8a7d302ebaa4cb619b0bf2e87527853/ACMP.cu#L588

this situation seems never happens. But if I change the code to while (depth_perturbed < params.depth_min || depth_perturbed > params.depth_max); the program will get stuck.

@GhiXu First of all, thank you for opening up the novel algorithm-ACMP！When I executed the acmp program, an error occurred, which caused the data processing to fail，as shown in the figure below：

The log displayed by the program seems to indicate that the memory of the GPU is not enough，and my computer GPU is RTX1070.If you can give some suggestions or solutions, I will be very grateful！The resolution of the image is 6000*4000.

Hi GhiXu, thank you for your great work! I tested the code and it really produce beautiful result! And Since I'm more familiar with colmap, so I modified your code to read colmap project directly, so I do not have to use the script to convert project type. I opened the project in https://github.com/CanCanZeng/ACMP_colmap, but I can delete it if you do not like it.

I looked into the depth fusion code, it's very similar to the original fusion code in colmap, and I set the parameters to the same, but the fused point cloud is much different, I can't find the cause. Have you find this situation?

The images I used is from https://github.com/openMVG/ImageDataset_SceauxCastle, and the SfM is done by colmap. the depth map is produced by ACMP on original resolution. the point cloud fused by ACMP and by colmap is much different the later is result from ACMP. I think the key parameters are reproj_error, relative_depth_diff, angle and num_consistent, I set them equal to colmap defaul value. I do not mean that the point cloud produced by colmap is better, I just want to know why they are so different.