3D object detection plays an important role in a large number of real-world
applications. It requires us to estimate the localizations and the orientations
of 3D objects in real scenes. In this paper, we present a new network
architecture which focuses on utilizing the front view images and frustum point
clouds to generate 3D detection results. On the one hand, a PointSIFT module is
utilized to improve the performance of 3D segmentation. It can capture the
information from different orientations in space and the robustness to
different scale shapes. On the other hand, our network obtains the useful
features and suppresses the features with less information by a SENet module.
This module reweights channel features and estimates the 3D bounding boxes more
effectively. Our method is evaluated on both KITTI dataset for outdoor scenes
and SUN-RGBD dataset for indoor scenes. The experimental results illustrate
that our method achieves better performance than the state-of-the-art methods
especially when point clouds are highly sparse.

本文介绍了一种新型网络架构，利用前视图像和视锥形点云生成三维检测结果，使用 PointSIFT 模块和 SENet 模块来提高三维分割的性能和有效地估计三维边界框，实验结果表明该方法在使用高度稀疏的点云时比现有方法表现更优，广泛应用于室内和室外的实际场景中的三维物体检测。

利用尺度不变和特征重新加权网络进行三维物体检测

3D Object Detection Using Scale Invariant and Feature Reweighting  Networks

Recently, 3D understanding research sheds light on extracting features from
point cloud directly, which requires effective shape pattern description of
point clouds. Inspired by the outstanding 2D shape descriptor SIFT, we design a
module called PointSIFT that encodes information of different orientations and
is adaptive to scale of shape. Specifically, an orientation-encoding unit is
designed to describe eight crucial orientations, and multi-scale representation
is achieved by stacking several orientation-encoding units. PointSIFT module
can be integrated into various PointNet-based architecture to improve the
representation ability. Extensive experiments show our PointSIFT-based
framework outperforms state-of-the-art method on standard benchmark datasets.
The code and trained model will be published accompanied by this paper.

该论文介绍了一种新的点云形状模式描述方法 ——PointSIFT 模块，该模块可以多尺度地描述点云的 8 个关键方向的信息，并被集成到各种 PointNet-based 架构中，从而提高了表示能力。实验表明我们的 PointSIFT 基于的框架在标准基准数据集上表现优于现有方法。