Comments (4)
Thank you for your interest! The predicted depth is scaled here.
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Thank you for your interest! The predicted depth is scaled here.
Thanks for you reply! The predicted depth is scaled only using 'normalize_scale' and 'scale_info', but the 'cam_model' with 4-channel map does not seem to be used. Do I understand correctly?
from metric3d.
Thank you for your interest! The predicted depth is scaled here.
Thanks for you reply! The predicted depth is scaled only using 'normalize_scale' and 'scale_info', but the 'cam_model' with 4-channel map does not seem to be used. Do I understand correctly?
The scale is adjusted here, where intrinsic (focal length) is used
from metric3d.
Thanks for your reply!
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Related Issues (20)
- Pixel represented focal length or real world scale focal length(mm) HOT 2
- Some problems in Training
- 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 1
- torch.hub.load error HOT 4
- Failed to find function: mono.model.backbones.convnext_large HOT 1
- Fine tune on custom dataset HOT 4
- Sparse GT depth from LiDAR for supervision? HOT 1
- Question regarding losses HOT 1
- Depth scale vs Metric scale HOT 5
- What does the pkl file contain in training with Matterport3D?
- 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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