Relying solely on off-the-shelf pre-trained models for depth prediction has some potential drawbacks. One drawback is that these models may not be optimized for specific use cases or datasets, leading to suboptimal performance when applied out-of-the-box. Additionally, pre-trained models may have biases or limitations based on the data they were trained on, which could affect their accuracy and generalization capabilities across different scenarios. Moreover, using off-the-shelf models may limit flexibility in adapting the depth estimation process to suit specific requirements or constraints of a particular application.