We propose a framework that operates entirely within pixel space to maintain edge sharpness and spatial integrity. 2. Methodology: Pixel-Space Diffusion
How high-level semantic cues guide the diffusion process to differentiate between overlapping object boundaries. Pixelpiece3
Traditional monocular depth models like Marigold often suffer from blurry edges and depth artifacts due to the lossy nature of VAEs. We propose a framework that operates entirely within
Implementation of a Diffusion Transformer (DiT) specifically tuned for depth map synthesis. 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.
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