Comparison against NYU Depth V2 and KITTI datasets.
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. Pixelpiece3
Detailed analysis of how bypassing latent-space compression removes "flying pixels" at depth discontinuities. 3. Quantitative and Qualitative Evaluation 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 We address the "flying pixel" artifact—a common byproduct
How high-level semantic cues guide the diffusion process to differentiate between overlapping object boundaries.
Implementation of a Diffusion Transformer (DiT) specifically tuned for depth map synthesis.
Traditional monocular depth models like Marigold often suffer from blurry edges and depth artifacts due to the lossy nature of VAEs.