Strange result of Mesh

[Zazexy]

I got a strange result of mesh by pretrained classifier (blender-lego_d8w512ext1pw0.1_lr0.0001bs2048iters50k/0049999.tar) and run_mv --config configs/blender.txt --scene lego --eval_only --denoise. When I open the obj file tsdf_mesh_test, it shows a sphere mesh as follow:

However, the ouput depth pictures are right I think.
Here is the detail output:
[train] ADE=1.2083 CD=976.5395 CD_median=862.2973 PSNR=nan SSIM=nan LPIPS=nan
[test] ADE=1.1905 CD=948.3585 CD_median=829.0297 PSNR=nan SSIM=nan LPIPS=nan
[train] ADE=1.1883 CD=930.2852 CD_median=807.2603 PSNR=nan SSIM=nan LPIPS=nan
[test] ADE=1.1760 CD=915.7480 CD_median=792.2218 PSNR=nan SSIM=nan LPIPS=nan
[train] ADE=1.1926 CD=898.6947 CD_median=770.4690 PSNR=nan SSIM=nan LPIPS=nan
[test] ADE=1.1712 CD=880.8589 CD_median=770.1740 PSNR=nan SSIM=nan LPIPS=nan
[train] ADE=1.1887 CD=906.7648 CD_median=788.7728 PSNR=nan SSIM=nan LPIPS=nan
[test] ADE=1.1785 CD=900.8463 CD_median=780.1147 PSNR=nan SSIM=nan LPIPS=nan
[train] ADE=1.2278 CD=1017.9339 CD_median=888.1369 PSNR=nan SSIM=nan LPIPS=nan
[test] ADE=1.2101 CD=999.7015 CD_median=880.9478 PSNR=nan SSIM=nan LPIPS=nan

[zhuomanliu]

It looks like you didn't load a pre-trained model of the ray-surface distance network. Loading a pre-trained classifier is to evaluate the classifier's accuracy or to train a ray-surface distance network. You can download the pre-trained model of ray-surface distance network from here (in the multiview folder) if you want to evaluate it directly without training.

[WuyiWong]

I got a strange result of mesh by pretrained classifier (blender-lego_d8w512ext1pw0.1_lr0.0001bs2048iters50k/0049999.tar) and run_mv --config configs/blender.txt --scene lego --eval_only --denoise. When I open the obj file tsdf_mesh_test, it shows a sphere mesh as follow: However, the ouput depth pictures are right I think. Here is the detail output: [train] ADE=1.2083 CD=976.5395 CD_median=862.2973 PSNR=nan SSIM=nan LPIPS=nan [test] ADE=1.1905 CD=948.3585 CD_median=829.0297 PSNR=nan SSIM=nan LPIPS=nan [train] ADE=1.1883 CD=930.2852 CD_median=807.2603 PSNR=nan SSIM=nan LPIPS=nan [test] ADE=1.1760 CD=915.7480 CD_median=792.2218 PSNR=nan SSIM=nan LPIPS=nan [train] ADE=1.1926 CD=898.6947 CD_median=770.4690 PSNR=nan SSIM=nan LPIPS=nan [test] ADE=1.1712 CD=880.8589 CD_median=770.1740 PSNR=nan SSIM=nan LPIPS=nan [train] ADE=1.1887 CD=906.7648 CD_median=788.7728 PSNR=nan SSIM=nan LPIPS=nan [test] ADE=1.1785 CD=900.8463 CD_median=780.1147 PSNR=nan SSIM=nan LPIPS=nan [train] ADE=1.2278 CD=1017.9339 CD_median=888.1369 PSNR=nan SSIM=nan LPIPS=nan [test] ADE=1.2101 CD=999.7015 CD_median=880.9478 PSNR=nan SSIM=nan LPIPS=nan

Hi ,could you please tell me how do you open the obj file tsdf_mesh_test, which shows a sphere mesh as provided. @Zazexy

[Zazexy]

@WuyiWong
Hi, I am glad to tell you that I use a tool called MeshLab to open the obj file.
@zhuomanliu
Thanks you for your response! I follow your guide and load pre-trained multi-view network models successfully. I have got the normal map and depth map.
However, when I run training coderun_mv.py --config configs/blender.txt --scene lego, I still get PSNR=nan SSIM=nan LPIPS=nan in ouput eval_metrics. How can I get these metrics.

[zhuomanliu]

@WuyiWong Hi, I am glad to tell you that I use a tool called MeshLab to open the obj file. @zhuomanliu Thanks you for your response! I follow your guide and load pre-trained multi-view network models successfully. I have got the normal map and depth map. However, when I run training coderun_mv.py --config configs/blender.txt --scene lego, I still get PSNR=nan SSIM=nan LPIPS=nan in ouput eval_metrics. How can I get these metrics.

You can refer to the answer in this issue.

[Zazexy]

Thank you so much！