HyperAIHyperAI

Command Palette

Search for a command to run...

5 months ago

3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

Iñigo Alonso Luis Riazuelo Luis Montesano Ana C. Murillo

3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

Abstract

Code Repositories

Shathe/3D-MiniNet
Official
tf
Mentioned in GitHub

Benchmarks

BenchmarkMethodologyMetrics
3d-semantic-segmentation-on-semantickitti3D-MiniNet
test mIoU: 55.8%
real-time-3d-semantic-segmentation-on-13D-MiniNet-tiny
Parameters (M): 0.44
Speed (FPS): 98
mIoU: 46.9
real-time-3d-semantic-segmentation-on-13D-MiniNet
Parameters (M): 3.97
Speed (FPS): 28
mIoU: 55.8

Build AI with AI

From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.

AI Co-coding
Ready-to-use GPUs
Best Pricing
Get Started

Hyper Newsletters

Subscribe to our latest updates
We will deliver the latest updates of the week to your inbox at nine o'clock every Monday morning
Powered by MailChimp
3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation | Papers | HyperAI