Pixelpiece3 -

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

Comparison against NYU Depth V2 and KITTI datasets. Pixelpiece3

Detailed analysis of how bypassing latent-space compression removes "flying pixels" at depth discontinuities. 3. Quantitative and Qualitative Evaluation How high-level semantic cues guide the diffusion process

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 By leveraging semantics-prompted diffusion

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