Researchers at ETH Zurich have made a breakthrough in the field of monocular depth estimation. They cleverly leveraged the Stable Diffusion open source Marigold model and achieved high-performance depth estimation without the need for real depth image training data by fine-tuning its denoising U-Net module. The innovation of this research is that it uses synthetic data to train the model and combines it with the affine invariant depth estimation method to effectively solve the error problem caused by the uncertainty of the camera's internal parameters and improve the model's general performance in unknown scenes. ization ability.
Researchers at ETH Zurich achieved innovation in monocular depth estimation by modifying the Stable Diffusion open source Marigold model. This model achieves excellent performance by fine-tuning the denoising U-Net module without requiring actual depth image training data. By training on synthetic data, Marigold can learn a wide range of scenarios and improve generalization capabilities on unseen data sets. The core technical idea is to use the prior knowledge of Stable Diffusion and adopt the affine invariant depth estimation method to eliminate the depth estimation error caused by the uncertainty of the camera's internal parameters.
This research result provides a new idea for monocular depth estimation technology. Its high efficiency and generalization ability are expected to be widely used in fields such as autonomous driving and robot navigation. Its future development is worth looking forward to. This research fully demonstrates the potential of the Stable Diffusion model and its application value in solving practical problems.