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

In this paper, we propose binary sparse convolutional networks called BSC-Net
for efficient point cloud analysis. We empirically observe that sparse
convolution operation causes larger quantization errors than standard
convolution. However, conventional network quantization methods directly
binarize the weights and activations in sparse convolution, resulting in
performance drop due to the significant quantization loss. On the contrary, we
search the optimal subset of convolution operation that activates the sparse
convolution at various locations for quantization error alleviation, and the
performance gap between real-valued and binary sparse convolutional networks is
closed without complexity overhead. Specifically, we first present the shifted
sparse convolution that fuses the information in the receptive field for the
active sites that match the pre-defined positions. Then we employ the
differentiable search strategies to discover the optimal opsitions for active
site matching in the shifted sparse convolution, and the quantization errors
are significantly alleviated for efficient point cloud analysis. For fair
evaluation of the proposed method, we empirically select the recently advances
that are beneficial for sparse convolution network binarization to construct a
strong baseline. The experimental results on Scan-Net and NYU Depth v2 show
that our BSC-Net achieves significant improvement upon our srtong baseline and
outperforms the state-of-the-art network binarization methods by a remarkable
margin without additional computation overhead for binarizing sparse
convolutional networks.

本文提出了一种基于二元稀疏卷积神经网络的点云分析方法，利用转移卷积并搜索一组最优操作次数以匹配预定义位置，从而缓解量化误差并实现网络二元化，取得了显著的性能提升。