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 title
Publication authorsKangcheng Liu, Ben M. Chen
Publication venuearXiv Preprint
Publication URL
Input Data TypesUses Geometry        Uses 3D
Programming language(s)C++
HardwareGTX 1080
Source code or download URL
Submission creation date3 May, 2021
Last edited18 May, 2021

3D semantic label results

Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow