PXDepth: Pixel-Space Modeling for Structure Preserving Monocular Depth Estimation

arXiv:2608.16984 · cs.CV, cs.AI, cs.GR · Submitted 2026-08-17 · Read on arXiv

cs.CV, cs.AI, cs.GR

Submitted: 2026-08-17

Updated: 2026-08-31

Comments: Project Page: https://yuanzhy29.github.io/PXDepth-Page/

Project page: https://yuanzhy29.github.io/PXDepth-Page

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 19× in this paper · explained in 1 episode
• Benchmark — 16× in this paper · explained in 2 episodes
• Transformer — 13× in this paper · explained in 2 episodes
• Fidelity — 12× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 11× in this paper
• Tokenization — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Generalization — 6× in this paper · explained in 13 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• European Conference on Computer Vision — 4× in this paper
• Foundation Model — 4× in this paper · explained in 3 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 4× in this paper · explained in 5 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• International Conference on Computer Vision — 3× in this paper
• Pixel-Perfect Depth — 3× in this paper
• Point Clouds — 3× in this paper · explained in 2 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Decode — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Two-Stage Framework — 1× in this paper · explained in 2 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
• Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation
• Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
• Lotus-2: Advancing Geometric Dense Prediction with Powerful Image Generative Model
• On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
• Auto-Encoding Variational Bayes
• SurGe: Improved Surface Geometry in Point Maps
• MoGe-3: Fine-Detail Monocular Geometry Estimation with Self-Guided Sparse Volumetric Refinement
• From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
• Depth Anything 3: Recovering the Visual Space from Any Views
• TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation
• DINOv3
• Masked Depth Modeling for Spatial Perception
• DIODE: A Dense Indoor and Outdoor DEpth Dataset
• Flow-Motion and Depth Network for Monocular Stereo and Beyond
• MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
• Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
• Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers

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