gangweix/pixel-perfect-depth

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1064 stars · 39 forks observed · 2026-08-28

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

Pixel-Perfect Depth is a monocular depth estimation model based on pixel-space diffusion transformers that produces flying-pixel-free depth maps and point clouds. It includes model weights, training, inference, and evaluation code released alongside a NeurIPS 2025 paper.

Use cases

• estimate depth maps from a single image
• generate point clouds from monocular depth without flying pixels
• run diffusion-based depth estimation on images
• evaluate monocular depth models on zero-shot benchmarks
• train a pixel-space diffusion depth model
• convert photos to 3D point clouds

When to choose

• you need high-quality depth maps with clean edges for 3D reconstruction or point clouds
• you want a state-of-the-art generative monocular depth model with released weights
• you're researching diffusion transformers for dense prediction

When to avoid

• you need real-time depth estimation on low-power hardware
• you want a simple lightweight discriminative depth model without GPU requirements
• you need a production-ready packaged application rather than research code

Facets

library · maturity active

machine-learning deep-learning computer-vision image-processing llm-inference computer-vision machine-learning deep-learning artificial-intelligence python monocular-depth-estimation diffusion-transformers point-clouds depth-estimation research-code neurips-2025 gpu linux

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markdown · JSON · MCP: product_card(name="gangweix/pixel-perfect-depth")