Efficient Monocular Depth Estimation Via Single-Step Latent Diffusion Models
Abstract
Monocular depth estimation is a fundamental task in computer vision, aiming to recover the depth information of a scene from a RGB image. Traditional latent diffusion models start from Gaussian noise and progressively restore depth information through iterative denoising, during which errors may accumulate, leading to blurred boundaries and the loss of fine details. In this work, we propose a method that directly predicts depth maps from RGB images by leveraging a pre-trained Stable Diffusion model as the generative backbone. The denoising U-Net of Stable Diffusion is redefined as an encoder-decoder architecture to enable more efficient feature extraction and reconstruction. The method reduces artifacts and cumulative errors while improving inference speed. In addition, the control of depth-specific features is enhanced through a CLIP-based conditional generation mechanism. Extensive evaluations on benchmark datasets demonstrate that the proposed approach achieves competitive performance in both accuracy and efficiency.
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