The techniques proposed in PERE can be extended to address domain shifts caused by factors beyond point number inconsistency by incorporating additional adaptation mechanisms. For instance, to account for variations in sensor configurations, the model can be trained with data augmentation techniques that simulate different sensor setups. This can help the model generalize better to unseen sensor configurations.
To handle changes in environmental conditions, the model can be trained on a diverse set of environmental scenarios to learn robust features that are invariant to environmental variations. Additionally, domain adaptation techniques like domain adversarial training can be employed to align feature distributions between different environmental conditions.
For addressing object occlusions, the model can be trained on datasets with varying levels of occlusions to learn to detect objects even when partially occluded. Techniques like occlusion-aware feature learning can be utilized to enhance the model's ability to detect objects under occlusion.
By incorporating these additional adaptation strategies, the PERE framework can be extended to handle domain shifts caused by a wide range of factors beyond point number inconsistency, making it more robust and adaptable to diverse real-world scenarios.