juno181
Regarding the discrepancy in experimental results of cut_roasted_beef in N3DV dataset.
Thank you for your excellent work. I encountered some issues during my experiments. The metric of cut_roasted_beef I got is { "SSIM": 0.9544036984443665, "SKSSIM": 0.9497656226158142, "SKSSIM2": 0.9747065305709839, "PSNR": 31.879066467285156, "LPIPS": 0.047466572374105453, "LPIPSVGG": 0.13830572366714478, "times": 0.01961996965110302 } There is a significant discrepancy from the PSNR of 33.73 provided in the paper. I used the code and commands you provided, and I would like to know how to obtain normal results.
The training configuration is as follows.
{
"name": "Python train cut_roasted_beef",
"type": "debugpy",
"request": "launch",
"program": "./train.py",
"console": "integratedTerminal",
"args": [
"-s", "/home/disk1/jh/E-D3DGS/dataset/cut_roasted_beef_ed3dgs",
"--config", "/home/disk1/jh/E-D3DGS/arguments/dynerf/cut_roasted_beef.py",
"--model_path", "/home/disk1/jh/E-D3DGS/output/cut_roasted_beef",
"--expname", "dynerf/cut_roasted_beef",
"-r", "2",
],
"python": "/home/disk1/jh/conda_envs/ed3dgs/bin/python"
}
Thanks for your experiment. This appears to be the same issue as #3. I am suspecting that the problem is that COLMAP is not producing the same results. I am currently revising the proprocessing and will update the code.
Thanks for your response！ Looking forward to your update.发自我的手机-------- 原始邮件 --------发件人： Juno @.>日期： 2024年11月24日周日 晚上9:33收件人： juno181/Ex4DGS @.>抄送： JiaoChenGuang-JiaoHan @.>, Author @.>主 题： Re: [juno181/Ex4DGS] Regarding the discrepancy in experimental results of cut_roasted_beef in N3DV dataset. (Issue #10) Thanks for your experiment. This appears to be the same issue as #3. I am suspecting that the problem is that COLMAP is not producing the same results. I am currently revising the proprocessing and will update the code.
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Thanks for your experiment. This appears to be the same issue as #3. I am suspecting that the problem is that COLMAP is not producing the same results. I am currently revising the proprocessing and will update the code.
Did you completely revise the processing code? I saw you submit a new pre_n3d_colmap.py, but I also have a bad result in coffee_martini. The metric of coffee_martini I got is PSNR: 28.13.
The new version of pre_n3d_colmap.py was submitted primarily to address issues related to resolution. However, the parts related to COLMAP densification are still being worked on and haven't been completely revised yet.
The new version of pre_n3d_colmap.py was submitted primarily to address issues related to resolution. However, the parts related to COLMAP densification are still being worked on and haven't been completely revised yet.
I found that the reconstruction evaluation results for dense inputs are much closer to those for sparse inputs in the paper, is this normal?
That's correct. This is because a dense input reduces the randomness of densification, which makes the result more stable. I am improving this by using COLMAP densification.
That's correct. This is because a dense input reduces the randomness of densification, which makes the result more stable. I am improving this by using COLMAP densification.
I am facing the same issue here. Is the COLMAP densification ready yet?
