Custom images with colmap get poor results.

[CuiYan27]

Describe the bug

I used DTU images(1600,1200) to build sdfstudio(meta_data.json) from colmap, tested neus-facto, and got a poor results.

Here is my process:

1. I ran the colmap(hloc) and got transfroms.json. The command is: ns-process-data images \ --data ./data/dtu/scan114/image \ --sfm_tool hloc \ --output-dir ./data/dtu/scan114/nerfstudio

2. I ran the _process_nerfstudio_tosdfstudio.py scripts to get meta_data.json. The command is: python scripts/datasets/process_nerfstudio_to_sdfstudio.py \ --data ./data/dtu/scan114/nerfstudio \ --output-dir ./data/dtu/scan114/sdfstudio \ --data-type colmap \ --scene-type object

3. Then I used neus-facto to start training the model. The command is: ns-train neus-facto \ --experiment-name dtu \ --trainer.max-num-iterations 20001 \ --trainer.save-only-latest-checkpoint False \ --trainer.steps_per_save 1000 \ --pipeline.model.sdf-field.inside-outside False \ --vis tensorboard sdfstudio-data \ --data ./data/dtu/scan114/sdfstudio

ns-train neus-facto \ --experiment-name dtu \ --trainer.max-num-iterations 20001 \ --trainer.save-only-latest-checkpoint False \ --trainer.steps_per_save 1000 \ --pipeline.model.sdf-field.inside-outside False \ --vis tensorboard nerfstudio-data \ --data ./data/dtu/scan114/nerfstudio

4. Finally, extract mesh with: ns-extract-mesh \ --load-config outputs/dtu/neus-facto/2023-12-28_170416/config.yml \ --output-path outputs/dtu/neus-facto/2023-12-28_170416/dtu.ply

ns-extract-mesh \ --load-config outputs/dtu/neus-facto/2023-12-28_164550/config.yml \ --output-path outputs/dtu/neus-facto/2023-12-28_164550/dtu.ply

But i get wrong mesh. Here is my results: nerfstudio-data:

sdfstudio-data:

Which step went wrong?

[peta-peta]

Could you please send a picture of the camera poses from the viewer? I figured out that the camera poses are not correctly transformed in step 2 (as per your description).

[CuiYan27]

Thank you for your reply! And I want to correct the size of my image, my input size is (384, 384).

I got the camera pose from colmap, and here is my camera pose. colmap: intrinsics: extrinsics:

nerfstudio data:

sdfstudio data:

[peta-peta]

Thanks! The pose looks good to me. Could you show me the pose in the nerfstudio viewer (after you have executed process_nerfstudio_to_sdfstudio.py), too?

[CuiYan27]

Here is the capture of the sdtstudio-data in viewer:

[CuiYan27]

video: https://github.com/autonomousvision/sdfstudio/assets/67808446/8cf67de9-ae0f-4fbe-89a4-7eea31c2fad6

[niujinshuchong]

Hi, I think it might due to the camera normalization. After using colmap, the pose is normalized to [-1, 1] cube but the real object might not be centered in the origin anymore. And in this case, the near and far might not be set correctly to cover the object. I think you can compare you colmap poses with the provided poses.

[hanjoonwon]

Hi, I think it might due to the camera normalization. After using colmap, the pose is normalized to [-1, 1] cube but the real object might not be centered in the origin anymore. And in this case, the near and far might not be set correctly to cover the object. I think you can compare you colmap poses with the provided poses.

Could you give me an example of how to fix it?

[CuiYan27]

Hi, I think it might due to the camera normalization. After using colmap, the pose is normalized to [-1, 1] cube but the real object might not be centered in the origin anymore. And in this case, the near and far might not be set correctly to cover the object. I think you can compare you colmap poses with the provided poses.

Could you give me an example of how to fix it?

I think you can use the idr preprocessing method. It can normalized pose to [-1, 1] cube.