jmanhype

Vggt Mps — Development skill for Claude Code

Development community

VGGT 3D Vision Agent optimized for Apple Silicon with Metal Performance Shaders.

How to install Vggt Mps

This entry records only its repository, not the path inside it, so there is no exact command to give. Open jmanhype/vggt-mps and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Vggt Mps does

VGGT 3D Vision Agent optimized for Apple Silicon with Metal Performance Shaders.

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README

vggt-mps

Port of Facebook Research's [VGGT](https://github.com/facebookresearch/vggt) (Visual Geometry Grounded Transformer) to Apple Silicon via PyTorch's MPS backend. Takes single or multi-view images and produces depth maps, camera poses, and 3D point clouds.


Version	2.0.0
Python	3.10+
Platform	macOS 13+ on Apple Silicon (M1/M2/M3)
Model	facebook/VGGT-1B (1B params, ~5 GB on disk)
License	MIT
PyPI	Not yet published

What it produces

Given N input images, VGGT predicts:

Output	Description
Depth maps	Per-pixel depth estimation
Camera poses	6-DOF camera parameters for each view
3D point clouds	Dense reconstruction (exportable as PLY, OBJ, GLB)
Confidence maps	Per-pixel reliability scores

Architecture

The upstream VGGT model is a 1B-parameter transformer trained on multi-view geometry tasks. This repo wraps it with:

MPS device detection and dtype handling (float32 for Metal compatibility)
A sparse attention module (vggt_sparse_attention.py) that patches the model at runtime for O(n) memory scaling instead of O(n^2)
A unified CLI (vggt command with subcommands)
A Gradio web interface
An MCP server for Claude Desktop integration

vggt-mps/
  src/
    vggt_core.py                # Core VGGT processing
    vggt_sparse_attention.py    # Runtime sparse attention patch
    config.py                   # Centralized configuration
    visualization.py            # 3D visualization
    commands/                   # CLI subcommands (demo, reconstruct, test, benchmark, web)
    utils/                      # Model loader, image utils, export
  tests/                        # MPS, sparse attention, integration tests
  repo/vggt/                    # Vendored upstream VGGT source

Sparse attention

The sparse attention module replaces standard O(n^2) cross-view attention with a covisibility-masked variant. No retraining required -- it patches the loaded