Comments (5)
Thanks for your amazing work!
- I really want to know the performance if you fine tuning the model on NYU datasets, I guess you can win the championship.
Have you ever tried this? If so, please tell me!- Will you make the training code available? Thank you very much!
- I am confused about the unit of fx and fy, is it (mm) or (pixels)?
Looking forward to your reply!
Thank you for your interest!
- We are working on this... But still KITTI and NYU are left along for zero-shot testing.
- The training codes will be available when we publish our next work.
- Focal lengths are by pixels.
from metric3d.
Thanks!
from metric3d.
Thank you for your work. Although you have already answered, I still want to ask whether the training code will be useful in the next few months
from metric3d.
Thank you for your work. Although you have already answered, I still want to ask whether the training code will be useful in the next few months
It should be if everything is going on well.
from metric3d.
We will release the code this week. @Ghul-huan
from metric3d.
Related Issues (20)
- Pixel represented focal length or real world scale focal length(mm) HOT 4
- Some problems in Training HOT 3
- Supporting old GPUs? HOT 3
- metric_scale in nyu.py HOT 1
- Speed Up Inference HOT 2
- NYU dataset and json HOT 1
- Inference Speed data
- normals not normal HOT 2
- Unable to adjust scale of depth correctly in the wild-mode HOT 1
- How to convert the DINO2reg-ViT model to an ONNX model HOT 2
- torch.hub.load error HOT 4
- Failed to find function: mono.model.backbones.convnext_large HOT 1
- Fine tune on custom dataset HOT 8
- Sparse GT depth from LiDAR for supervision? HOT 1
- Question regarding losses HOT 1
- Depth scale vs Metric scale HOT 6
- What does the pkl file contain in training with Matterport3D? HOT 1
- generate only a depth matrix without generating a 3D point cloud HOT 2
- Is there any reference code to generate kitti dataset annotation?
- Camera parameters of taskonomy HOT 2
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