jinwoolee1230

jinwoolee1230 / POLI

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[RSS 2026] Learning Point Cloud Geometry as a Statistical Manifold: Theory and Practice

3d-vision lidar perception point-cloud robotics
27
0
100% credibility
Found May 12, 2026 at 27 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

POLI is a self-supervised neural network that predicts Gaussian ellipsoids for points in LiDAR scans to enhance robotic 3D perception tasks including registration, odometry, and object pose estimation.

How It Works

1
🔍 Discover POLI

You stumble upon this clever tool while hunting for smarter ways to make sense of robot scans in 3D space.

2
📥 Grab ready models

Download the pre-trained helpers that already know how to guess shapes from blurry point clouds.

3
🚀 Run a fun demo

Pick a sample scan pair and launch a quick show to align them perfectly.

4
✨ See shapes come alive

Watch sparse dots transform into smooth, confident 3D matches that boost robot vision instantly.

5
🧪 Test your own scans

Feed in your robot's real-world data for pose guesses or path tracking.

🎉 Robot sees sharper

Your 3D tasks like matching scenes or finding objects now work better without any teaching data.

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Star Growth

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AI-Generated Review

What is POLI?

POLI predicts a Gaussian ellipsoid per point in sparse LiDAR clouds, modeling local geometry as a statistical manifold via a self-supervised Python network built on PointNet++. It plugs into pipelines for global registration, LiDAR odometry, and object pose estimation, boosting accuracy without labels or tweaks—use pretrained checkpoints for Velodyne or Ouster sensors and run demos like FPFH+ROBIN for RSS 2026-style results on KITTI or HeLiPR data.

Why is it gaining traction?

Zero-effort integration stands out: densify scans for feature matching, feed covariances to GICP odometry, or derive normals for pose tasks, with animated Iridescence viewers showing real gains. RSS 2026 acceptance and ready-to-run scripts (e.g., `python applications/lidar_odometry/POLI_GICP.py`) hook robotics devs seeking geometry without supervision—beats hand-crafted stats or labeled training.

Who should use this?

Robotics engineers tuning LiDAR odometry or SLAM on sparse sensors like Velodyne in drones/autonomous cars. Perception teams estimating object poses from partial scans in manipulation pipelines. SLAM devs preprocessing KITTI/HeLiPR data for registration.

Verdict

Grab it for demos if you're in LiDAR perception—pretrained weights and scripts deliver quick wins despite 27 stars and 1.0% credibility score. Early-stage with solid docs; train custom models once tests expand.

(198 words)

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