This paper explores the transition from latent-space diffusion models to pixel-space diffusion generation . We address the "flying pixel" artifact—a common byproduct of Variational Autoencoder (VAE) compression—by performing diffusion directly in the pixel domain. By leveraging semantics-prompted diffusion , our approach ensures high-quality point cloud reconstruction from single-view images. 1. Introduction

Comparison against NYU Depth V2 and KITTI datasets.

Since "Pixelpiece3" appears to be a user-specific project name or a very niche reference, I've drafted a "deep paper" structure based on the most likely technical context: . This topic aligns with recent breakthroughs in monocular depth estimation that move away from latent-space artifacts. Draft: Pixel-Perfect Monocular Depth Estimation

Visual evidence of reduced noise and sharper depth transitions compared to state-of-the-art latent models. 4. Conclusion

How high-level semantic cues guide the diffusion process to differentiate between overlapping object boundaries.

We propose a framework that operates entirely within pixel space to maintain edge sharpness and spatial integrity. 2. Methodology: Pixel-Space Diffusion

Implementation of a Diffusion Transformer (DiT) specifically tuned for depth map synthesis.

Detailed analysis of how bypassing latent-space compression removes "flying pixels" at depth discontinuities. 3. Quantitative and Qualitative Evaluation