magicleap/SuperPointPretrainedNetwork

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2185 stars · 423 forks observed · 2026-08-28

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

A PyTorch pre-trained implementation of the SuperPoint fully convolutional neural network for real-time interest point detection and descriptor computation, with a demo script for sparse optical flow tracking across image sequences. It includes a weights file and Python deployment script supporting image directories, video files, and webcam input.

Use cases

• detect interest points in images with a pretrained neural network
• compute descriptors for image-to-image feature matching
• track sparse points across video frames for optical flow
• build visual SLAM or VO front-ends
• run feature detection on webcam or video files in real time

When to choose

• you need a self-supervised learned alternative to hand-crafted feature detectors like SIFT or ORB
• you want a simple PyTorch script to evaluate SuperPoint on your own image or video data
• you are prototyping sparse point tracking for SLAM, VO, or image matching research

When to avoid

• you need a maintained production library with API stability and active support
• you require dense optical flow rather than sparse point tracking
• you need a non-PyTorch framework or mobile/embedded deployment out of the box

Facets

library · maturity maintenance

machine-learning computer-vision image-processing computer-vision machine-learning deep-learning python superpoint feature-detection interest-points optical-flow slam pytorch pretrained-model feature-matching linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="magicleap/SuperPointPretrainedNetwork")