This paper presents Pixel-Perfect Depth, a monocular depth estimation model based on pixel-space diffusion generation that produces high-quality, flying-pixel-free point clouds from estimated depth maps. Current generative depth estimation models fine-tune Stable Diffusion and achieve impressive performance. However, they require a VAE to compress depth maps into latent space, which inevitably introduces \textit{flying pixels} at edges and details. Our model addresses this challenge by directly performing diffusion generation in the pixel space, avoiding VAE-induced artifacts. To overcome the high complexity associated with pixel-space generation, we introduce two novel designs: 1) Semantics-Prompted Diffusion Transformers (SP-DiT), which incorporate semantic representations from vision foundation models into DiT to prompt the diffusion process, thereby preserving global semantic consistency while enhancing fine-grained visual details; and 2) Cascade DiT Design that progressively increases the number of tokens to further enhance efficiency and accuracy. Our model achieves the best performance among all published generative models across five benchmarks, and significantly outperforms all other models in edge-aware point cloud evaluation.

翻译：本文提出像素级完美深度（Pixel-Perfect Depth），一种基于像素空间扩散生成的单目深度估计模型，能够从估计的深度图中生成高质量、无漂浮像素的点云。当前基于生成式方法的深度估计模型通常对Stable Diffusion进行微调，并取得了令人瞩目的性能。然而，这些方法需要借助变分自编码器（VAE）将深度图压缩至潜在空间，这不可避免地会在边缘和细节处引入“漂浮像素”。我们的模型通过直接在像素空间进行扩散生成来解决这一挑战，从而避免了VAE引入的伪影。为了克服像素空间生成带来的高计算复杂度，我们引入了两项新颖的设计：1）语义提示扩散Transformer（SP-DiT），该设计将来自视觉基础模型的语义表征融入DiT中，以引导扩散过程，从而在增强细粒度视觉细节的同时保持全局语义一致性；2）级联DiT设计，通过逐步增加令牌数量以进一步提升效率和精度。我们的模型在五个基准测试中均优于所有已发布的生成式模型，并在边缘感知点云评估中显著超越所有其他模型。