Comments (6)
Hi, this result is very strange, can you provide me with more details? For example, pytorch version, operating environment.
Besides that, I also doubt whether the pre-trained model is loaded normally. Can you make sure that this line of code runs successfully?
Line 443 in 38c1b90
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Thanks for your reply.
Yes the pretrained model is loaded successfully. To check this problem, we set "strict=True" and get the same results.
We run the code on an ubuntu server and Nvidia 3090 GPU. Pytorch version is the same as in readme, and other packages that may have influence are show as following:
All the evaluation outputs on DTU dataset are shown as following:
The rendered images seem to be reasonable:
Scan114_32_0.png
scan45_44_0.png
What's more, the evaluation results on nerf_llff_data (32 images for evaluation in total) and nerf_synthetic_data (32 images for evaluation in total) are also different from the psnr, ssim & lpips results that are shown on paper:
Also, the rendered images seem to be reasonable:
chair_32_0.png (same as the image shown on the supplementary materials)
fortress_25_0.png
We are writing a paper and prepare to cite your paper and compare with yours results, so we want to check this problem. Thanks.
from enerf.
While the model appears to perform well on the other two datasets, there may be an issue with the format of the DTU dataset when it comes to the artifacts. Based on the provided rendering images, which are clearly 512x640 in size, it is possible that the camera pose scale is incorrect. To confirm this, could you please review the content of $workspace/dtu/Cameras/train/00000000_cam.txt.
0.970263 0.00747983 0.241939 -191.02
-0.0147429 0.999493 0.0282234 3.28832
-0.241605 -0.030951 0.969881 22.5401
0.0 0.0 0.0 1.0
intrinsic
361.54125 0.0 82.900625
0.0 360.3975 66.383875
0.0 0.0 1.0
425.0 2.5
from enerf.
We follow the readme to download the dtu, llff, nerf dataset. The camera paramters are shown as follows. It seems that there is no difference to your data.
We download the code, pretrained model and dataset, then we run the rendering command directly after specifying the dataset path. We didn't modify the dataset or the code.
from enerf.
I'm sorry, it's my fault. I must have introduced a bug during my later update. However, I need to go to bed now and don't have time to locate where the bug is. A quick but temporary solution is to run "git checkout 2d6b3b2" and then execute the evaluation command.
I will address the issue tomorrow and update it on the master branch.
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I have solved this bug.
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Related Issues (20)
- custom outdoor dataset
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- Camera Color Calibration
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- 关于zjumocap_train.yaml文件下某些项的作用 HOT 1
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- weird result after visualizing on the ENeRF-Outdoor dataset
- Problems with visualizing outdoor video HOT 1
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