The KITTI Vision Benchmark Suite

The task of detecting 3D objects in point cloud
has a pivotal role in many real-world
applications. However, 3D object detection
performance is behind that of 2D object detection
due to the lack of powerful 3D feature extraction
methods. In order to address this issue, we
propose to build a 3D backbone network to learn
rich 3D feature maps by using sparse 3D CNN
operations for 3D object detection in point
cloud. The 3D backbone network can inherently
learn 3D features from almost raw data without
compressing point cloud into multiple 2D images
and generate rich feature maps for object
detection. The sparse 3D CNN takes full
advantages of the sparsity in the 3D point cloud
to accelerate computation and save memory, which
makes the 3D backbone network achievable.
Empirical experiments are conducted on the KITTI
benchmark and results show that the proposed
method can achieve state-of-the-art performance
for 3D object detection.

Updating later

@article{DBLP:journals/corr/abs-1901-08373,
author = {Xuesong Li and
Jos{\'{e}} E. Guivant and
Ngaiming Kwok and
Yongzhi Xu},
title = {3D Backbone Network for 3D Object
Detection},
journal = {CoRR},
volume = {abs/1901.08373},
year = {2019},
url = {http://arxiv.org/abs/1901.08373},
archivePrefix = {arXiv},
eprint = {1901.08373},
timestamp = {Sat, 02 Feb 2019 16:56:00 +0100},
biburl =
{https://dblp.org/rec/bib/journals/corr/abs-1901-
08373},
bibsource = {dblp computer science
bibliography, https://dblp.org}
}