Sihang-Geng

Sihang-Geng / Point-Cloud-Affordance-Annotator

Public

A lightweight Open3D/Tkinter tool for sparse point-cloud annotation and affordance diffusion heatmap generation.

3d-vision affordance annotation-tool open3d point-cloud
11
1
100% credibility
Found May 03, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A graphical desktop tool for selecting sparse points on 3D point clouds and diffusing them into dense, colorful affordance heatmaps to label interactive regions on objects.

How It Works

1
🔍 Discover the labeling tool

You find a simple app that helps mark where you can grab or interact with 3D scans of everyday objects, perfect for robotics projects.

2
💻 Set up on your computer

You download and prepare the tool so it's ready to use, just like installing any helpful program.

3
🚀 Start with a sample object

You launch the demo and load a sample 3D object to see how it works right away.

4
🎯 Pick a few key spots

In the 3D viewer, you hold shift and click just a handful of points on areas like handles or buttons that feel interactive.

5
⏎ Finish selecting and go back

You press Q to wrap up your picks and return to the main window feeling excited.

6
🌈 Create the magic heatmap

You tweak a couple sliders for spread and color, then click to diffuse your picks into a beautiful, smooth map highlighting the whole interactive zone.

7
💾 Save your labeled model

You click save to store the colorful 3D file with scores, ready for your next steps.

✅ Labels ready for action

Now you have quick, dense labels on your 3D objects, making dataset building or robot training a breeze.

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Star Growth

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AI-Generated Review

What is Point-Cloud-Affordance-Annotator?

This Python tool, built with Open3D and Tkinter, lets you sparsely click a few points on a 3D point cloud to annotate affordance regions—like handles or interaction zones—then diffuses them into dense heatmaps. It solves the tedium of labeling large point clouds for robotics or vision tasks by turning minimal seeds into smooth probability fields. Drop in PLY files, tweak diffusion params like k-neighbors and alpha, and export colored PLY outputs with affordance scores.

Why is it gaining traction?

Its lightweight Open3D/Tkinter setup spins up fast without heavy deps, unlike bloated annotation suites, offering instant feedback on diffusion tweaks via a modern GUI with log panel and colormaps. Batch mode scans datasets for specific PLY files, resuming from folders, while single-file demos make prototyping affordance heatmaps dead simple. Devs dig the clean inputs/outputs workflow for quick sanity checks on point-cloud interaction regions.

Who should use this?

Robotics engineers labeling affordance datasets for grasping or manipulation experiments. CV researchers needing sparse-to-dense point-cloud annotations for training diffusion models. Teams doing visual validation of interaction heatmaps on objects like knives or rings.

Verdict

Grab it for lightweight point-cloud affordance annotation if you're in robotics—solid docs, MIT license, and demo-ready despite 11 stars and 1.0% credibility score signaling early maturity. Test on samples first; lacks tests but shines for rapid prototyping.

(178 words)

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