Submitted by Kangcheng Liu.

Submission data

Full nameFeature_GeometricNet: Fast Large-Scale LiDAR Point Clouds Understanding Network
DescriptionThis work presents FG-Net, a general deep learning framework for large-scale point clouds understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 GPU. First, a novel noise and outlier filtering method is designed to facilitate subsequent high-level tasks. For effective understanding purpose, we propose a deep convolutional neural network leveraging correlated feature mining and deformable convolution based geometric-aware modelling, in which the local feature relationships and geometric patterns can be fully exploited. For the efficiency issue, we put forward an inverse density sampling operation and a feature pyramid based residual learning strategy to save the computational cost and memory consumption respectively.
Publication titlehttps://arxiv.org/abs/2012.09439
Publication authorsKangcheng Liu, Ben M. Chen
Publication venuearXiv Preprint
Publication URLhttps://arxiv.org/abs/2012.09439
Input Data TypesUses Geometry        Uses 3D
Programming language(s)C++
HardwareGTX 1080
Websitehttps://github.com/KangchengLiu/Feature-Geometric-Net-FG-Net
Source code or download URLhttps://github.com/KangchengLiu/Feature-Geometric-Net-FG-Net
Submission creation date3 May, 2021
Last edited18 May, 2021

3D semantic label results

Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow
permissive0.6900.8840.7540.7950.6470.8180.4220.8020.6120.6040.9450.4620.1890.5630.8530.7260.7650.6320.9040.8210.606