Pixelpiece3 (Linux RECOMMENDED)

Comparison against NYU Depth V2 and KITTI datasets.

Traditional monocular depth models like Marigold often suffer from blurry edges and depth artifacts due to the lossy nature of VAEs. Pixelpiece3

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 Comparison against NYU Depth V2 and KITTI datasets

Implementation of a Diffusion Transformer (DiT) specifically tuned for depth map synthesis. This topic aligns with recent breakthroughs in monocular

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

Moving diffusion to the pixel space represents a significant leap in the fidelity of generated depth maps. This has direct implications for high-resolution 3D reconstruction and augmented reality applications where depth precision is paramount.