Pixelpiece3 Guide

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

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

We propose a framework that operates entirely within pixel space to maintain edge sharpness and spatial integrity. 2. Methodology: Pixel-Space Diffusion Pixelpiece3

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.

Implementation of a Diffusion Transformer (DiT) specifically tuned for depth map synthesis. How high-level semantic cues guide the diffusion process

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 Conclusion We propose a framework that operates entirely

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