Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network
  Leveraging Correlated Feature Mining and Geometric-Aware Modelling   This 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. Extensive experiments on real-world challenging
datasets demonstrated that our approaches outperform state-of-the-art
approaches in terms of accuracy and efficiency. Moreover, weakly supervised
transfer learning is also conducted to demonstrate the generalization capacity
of our method.

25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an