Monocular depth estimation is a challenging task that predicts the pixel-wise depth from a single 2D image. Current methods typically model this problem as a regression or classification task. We propose DiffusionDepth, a new approach that reformulates monocular depth estimation as a denoising diffusion process. It learns an iterative denoising process to `denoise' random depth distribution into a depth map with the guidance of monocular visual conditions. The process is performed in the latent space encoded by a dedicated depth encoder and decoder. Instead of diffusing ground truth (GT) depth, the model learns to reverse the process of diffusing the refined depth of itself into random depth distribution. This self-diffusion formulation overcomes the difficulty of applying generative models to sparse GT depth scenarios. The proposed approach benefits this task by refining depth estimation step by step, which is superior for generating accurate and highly detailed depth maps. Experimental results on KITTI and NYU-Depth-V2 datasets suggest that a simple yet efficient diffusion approach could reach state-of-the-art performance in both indoor and outdoor scenarios with acceptable inference time.

翻译：单目深度估计是一项从单张二维图像中预测逐像素深度的挑战性任务。现有方法通常将此问题建模为回归或分类任务。我们提出DiffusionDepth，这是一种将单目深度估计重新构建为去噪扩散过程的新方法。该方法学习一个迭代去噪过程，在单目视觉条件的引导下，将随机深度分布“去噪”为深度图。该过程在由专用深度编码器和解码器编码的潜在空间中执行。模型并非对真实深度进行扩散，而是学习逆转将自身精炼深度扩散为随机深度分布的过程。这种自扩散公式克服了将生成模型应用于稀疏真实深度场景的难题。所提方法通过逐步精炼深度估计使该任务受益，尤其擅长生成精确且高度细节化的深度图。在KITTI和NYU-Depth-V2数据集上的实验结果表明，一种简单而高效的扩散方法可在室内外场景中达到最优性能，且推理时间可接受。