Comments (1)
The intrinsic and extrinsic camera calibration is a hyperparameter to the network, see the very bottom of the README.
Training on one camera configuration and dataset, but then inferencing on another camera configuration will most likely worsen performance.
It would therefore be suggested to train on a custom dataset with your own camera configuration. This also allows you to choose a more suitable resolution.
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Related Issues (20)
- how to generate this simulated data
- Frame Rate HOT 4
- Training of deeplab-mobilenet and deeplab-xception failed HOT 2
- What changes to make to resume training from where it was left off? HOT 2
- Non-360 View HOT 10
- Frame rate HOT 2
- Training on original input HOT 2
- How to test on real-world images HOT 1
- Performance when evaluating custom data HOT 1
- BEV image HOT 8
- some issue about "homography_converter" HOT 10
- What should the regularization coefficient be set to? HOT 1
- drone camera config file HOT 2
- A few questions about your work HOT 1
- 0
- Training on Google Colab HOT 2
- field of view single-input model HOT 2
- the R matrix calculate order may have some problem? HOT 1
- Model for testing HOT 1
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