Pixelpiece3 May 2026

Comparison against NYU Depth V2 and KITTI datasets.

Implementation of a Diffusion Transformer (DiT) specifically tuned for depth map synthesis. Pixelpiece3

How high-level semantic cues guide the diffusion process to differentiate between overlapping object boundaries. Comparison against NYU Depth V2 and KITTI datasets

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 Pixelpiece3