Submitted by kangc Liu.

Submission data

Full nameFG-Net on Scannet
DescriptionWe present 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 8G GPU and an i7 CPU. First, a novel noise and outlier filtering method is designed to facilitate the subsequent high-level understanding tasks. For effective understanding purpose, we propose a novel plug-and-play module consisting of correlated feature mining and deformable convolution based geometric-aware modelling, in which the local feature relationships and point clouds geometric structures can be fully extracted and exploited. For the efficiency issue, we put forward a new composite inverse density sampling based and learning based 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 date27 Jun, 2021
Last edited28 Jun, 2021

3D semantic label results

Infoavg ioubathtubbedbookshelfcabinetchaircountercurtaindeskdoorfloorotherfurniturepicturerefrigeratorshower curtainsinksofatabletoiletwallwindow