We propose a framework that operates entirely within pixel space to maintain edge sharpness and spatial integrity. 2. Methodology: Pixel-Space Diffusion
Comparison against NYU Depth V2 and KITTI datasets. Pixelpiece3
Implementation of a Diffusion Transformer (DiT) specifically tuned for depth map synthesis. We propose a framework that operates entirely within
How high-level semantic cues guide the diffusion process to differentiate between overlapping object boundaries. Pixelpiece3
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