zhyever/PatchFusion

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1025 stars · 64 forks observed · 2026-10-04

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PatchFusion is a CVPR 2024 end-to-end tile-based framework for high-resolution monocular metric depth estimation from single images. It fuses globally-consistent coarse depth predictions with finer tiled predictions and works on top of base models like ZoeDepth and Depth-Anything.

Use cases

• estimate metric depth maps from high-resolution images
• run monocular depth estimation on 4K photos
• improve ZoeDepth or Depth-Anything depth predictions on large images
• train a depth estimation model on custom datasets
• generate depth maps for depth-guided image generation

When to choose

• you need metric (not just relative) depth from single images at high resolution
• existing depth models lose detail or consistency on large images
• you want a research-grade, model-agnostic tiling framework with training code

When to avoid

• you only need fast real-time depth on low-resolution video
• you need a production-ready plug-and-play API rather than a research codebase
• you lack a GPU, since inference is compute-intensive

Facets

library · maturity active

computer-vision image-processing machine-learning deep-learning computer-vision machine-learning image-processing artificial-intelligence python cross-platform depth-estimation monocular-depth metric-depth tile-based-inference high-resolution cvpr-2024 pytorch research-code gpu linux

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