Efficient analysis of point clouds holds paramount significance in real-world
3D applications. Currently, prevailing point-based models adhere to the
PointNet++ methodology, which involves embedding and abstracting point features
within a sequence of spatially overlapping local point sets, resulting in
noticeable computational redundancy. Drawing inspiration from the streamlined
paradigm of pixel embedding followed by regional pooling in Convolutional
Neural Networks (CNNs), we introduce a novel, uncomplicated yet potent
architecture known as PointGL, crafted to facilitate efficient point cloud
analysis. PointGL employs a hierarchical process of feature acquisition through
two recursive steps. First, the Global Point Embedding leverages
straightforward residual Multilayer Perceptrons (MLPs) to effectuate feature
embedding for each individual point. Second, the novel Local Graph Pooling
technique characterizes point-to-point relationships and abstracts regional
representations through succinct local graphs. The harmonious fusion of
one-time point embedding and parameter-free graph pooling contributes to
PointGL's defining attributes of minimized model complexity and heightened
efficiency. Our PointGL attains state-of-the-art accuracy on the ScanObjectNN
dataset while exhibiting a runtime that is more than 5 times faster and
utilizing only approximately 4% of the FLOPs and 30% of the parameters compared
to the recent PointMLP model. The code for PointGL is available at
this https URL

PointGL 是一种高效的点云分析架构，通过全局点嵌入和局部图池化两个递归步骤实现特征获取，并在 ScanObjectNN 数据集上取得了最新 PointMLP 模型的五倍速度和较少的计算资源使用的最先进准确性。

PointGL: 一个简单的全局 - 局部框架用于高效的点云分析

PointGL: A Simple Global-Local Framework for Efficient Point Cloud  Analysis

Recent advances on 3D object detection heavily rely on how the 3D data are
represented, \emph{i.e.}, voxel-based or point-based representation. Many
existing high performance 3D detectors are point-based because this structure
can better retain precise point positions. Nevertheless, point-level features
lead to high computation overheads due to unordered storage. In contrast, the
voxel-based structure is better suited for feature extraction but often yields
lower accuracy because the input data are divided into grids. In this paper, we
take a slightly different viewpoint -- we find that precise positioning of raw
points is not essential for high performance 3D object detection and that the
coarse voxel granularity can also offer sufficient detection accuracy. Bearing
this view in mind, we devise a simple but effective voxel-based framework,
named Voxel R-CNN. By taking full advantage of voxel features in a two stage
approach, our method achieves comparable detection accuracy with
state-of-the-art point-based models, but at a fraction of the computation cost.
Voxel R-CNN consists of a 3D backbone network, a 2D bird-eye-view (BEV) Region
Proposal Network and a detect head. A voxel RoI pooling is devised to extract
RoI features directly from voxel features for further refinement. Extensive
experiments are conducted on the widely used KITTI Dataset and the more recent
Waymo Open Dataset. Our results show that compared to existing voxel-based
methods, Voxel R-CNN delivers a higher detection accuracy while maintaining a
real-time frame processing rate, \emph{i.e}., at a speed of 25 FPS on an NVIDIA
RTX 2080 Ti GPU. The code is available at
https://github.com/djiajunustc/Voxel-R-CNN.

本文提出了一种基于体素的框架 ——Voxel R-CNN，通过从体素特征中提取 RoI 特征，实现与最先进的基于点的模型相当的检测准确度，但计算成本只有一小部分，并在 KITTI 数据集上实现了更高的检测准确度和实时图像处理速率。