"No valid points" when running demo on example data about neuralrecon HOT 11 CLOSED

The issue should be fixed by the latest commit. You can pull the latest version and try again. Sorry for the caused trouble.
To provide some details, the X-Y plane in the ARKit coordinates should be moved downwards a little to meet the training settings on ScanNet.

Hello! Latest commit fixed an error, but the results are still unrecognizable.
Should results on demo data look like this? https://drive.google.com/file/d/1g-woNtSl8duVeV2IETtxlDB-PjVmYo4a/view?usp=sharing

It appears that the overall shape is correct and the problem is on the shading mode in MeshLab. You can try to use this shading configuration in MeshLab:

The released demo data is the data used in the header video on the project page. The corresponding mesh is also hosted on Skechfab.

Turns out I loaded all the generated meshes at once. Now everything's fine, thank you for the quick response!

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I reloaded demo data, and it worked fine after that.

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@JiamingSuen Thank you for looking into this super fast. It works great, and I was able to test it on my own captured data as well. Awesome work!

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I get the same error, for all the incremental saves and final save, with or without visualization

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It is possible to get images and intermediate meshes by adding a condition before save_mesh_scene.

	if "scene_tsdf" in outputs:
		save_mesh_scene(outputs, sample, epoch_idx)

However, the result is completely unrecognizable.

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The issue should be fixed by the latest commit. You can pull the latest version and try again. Sorry for the caused trouble.

To provide some details, the X-Y plane in the ARKit coordinates should be moved downwards a little to meet the training settings on ScanNet.

UPDATE: Please notice that the data preprocessing needs to be run again after pulling the latest code, which can be achieved by simply deleting SyncedPoses.txt in the data folder.

from neuralrecon.

The issue should be fixed by the latest commit. You can pull the latest version and try again. Sorry for the caused trouble.

To provide some details, the X-Y plane in the ARKit coordinates should be moved downwards a little to meet the training settings on ScanNet.

Hello! Latest commit fixed an error, but the results are still unrecognizable.
Should results on demo data look like this? https://drive.google.com/file/d/1g-woNtSl8duVeV2IETtxlDB-PjVmYo4a/view?usp=sharing

from neuralrecon.

The issue should be fixed by the latest commit. You can pull the latest version and try again. Sorry for the caused trouble.
To provide some details, the X-Y plane in the ARKit coordinates should be moved downwards a little to meet the training settings on ScanNet.

Hello! Latest commit fixed an error, but the results are still unrecognizable.
Should results on demo data look like this? https://drive.google.com/file/d/1g-woNtSl8duVeV2IETtxlDB-PjVmYo4a/view?usp=sharing

It appears that the overall shape is correct and the problem is on the shading mode in MeshLab. You can try to use this shading configuration in MeshLab:

The released demo data is the data used in the header video on the project page. The corresponding mesh is also hosted on Skechfab.

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@ftasse Please confirm that you have also been able to run the demo with the latest commit, thanks!

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@JiamingSuen Thank you for looking into this super fast. It works great, and I was able to test it on my own captured data as well. Awesome work!

Thanks for your reply (and also for raising this issue)! Glad that you like our work!

from neuralrecon.

The issue should be fixed by the latest commit. You can pull the latest version and try again. Sorry for the caused trouble.

To provide some details, the X-Y plane in the ARKit coordinates should be moved downwards a little to meet the training settings on ScanNet.

UPDATE: Please notice that the data preprocessing needs to be run again after pulling the latest code, which can be achieved by simply deleting SyncedPoses.txt in the data folder.

Hello, thank you for your great work.
Could you explain that why the ARKit coordinates should be moved downwards a little.
BTW, when we run NeuralRecon on our own data, it occurred no valid points: scale 1/2 like this issue.
Whether should we pre-process our own data to meet some kind of training settings on ScanNet ?
Could you provide the training setting in detail?

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