TPDepth: Leveraging Text Prompts with ControlNet to Boost Diffusion-based Depth Estimation

Recent diffusion-based methods have shown strong ability in the depth estimation task, but they largely overlook the rich textual priors embedded in pretrained diffusion models that can enhance both performance and robustness in diverse scenes. In this paper, we propose TPDepth, a diffusion-based, affine-invariant monocular depth estimator that incorporates textual semantics via a Text-Prompted ControlNet. While directly injecting text into the diffusion U-Net can cause the network to over-attend to local semantic cues and compromise global structural modeling, TPDepth processes textual features through a separate ControlNet branch, allowing semantic information to be incorporated without disrupting the spatial reasoning pipeline. Prompt-conditioned features are modulated by an Adaptive Control Scale Module(ACSM) and injected into decoder of the diffusion UNet with skip connections. The model is fine-tuned with a fixed timestep for deterministic prediction. TPDepth achieves state-of-the-art results on NYUv2, KITTI, and ScanNet, and demonstrates competitive performance on two additional zero-shot benchmarks using only 61K training images. Code and models can be found on our https://github.com/Lioely/TPDepth project page.