论文标题
重新思考点网嵌入更快，紧凑的模型
Rethinking PointNet Embedding for Faster and Compact Model
论文作者
论文摘要
PointNet是广泛使用的点嵌入方法，被称为连续集合函数的通用近似器，可以每秒处理100万点。然而，对于包括PointNet在内的现有基于神经网络的方法，对最近开发高性能传感器的最新开发的实时推断仍然具有挑战性。在普通情况下，PointNet的嵌入函数的行为就像软尖端函数，当输入点存在于输入空间的某个局部区域中时，该功能被激活。利用此属性，我们通过用高斯内核用软指导函数替换PointNet的嵌入功能来降低点嵌入的计算成本。此外，我们表明高斯内核也满足了点网所满足的通用近似定理。在实验中，我们验证了使用高斯内核的模型与基线方法可比结果可比，但每个样本的浮点操作少得多，最多可从PointNet降低92％。
PointNet, which is the widely used point-wise embedding method and known as a universal approximator for continuous set functions, can process one million points per second. Nevertheless, real-time inference for the recent development of high-performing sensors is still challenging with existing neural network-based methods, including PointNet. In ordinary cases, the embedding function of PointNet behaves like a soft-indicator function that is activated when the input points exist in a certain local region of the input space. Leveraging this property, we reduce the computational costs of point-wise embedding by replacing the embedding function of PointNet with the soft-indicator function by Gaussian kernels. Moreover, we show that the Gaussian kernels also satisfy the universal approximation theorem that PointNet satisfies. In experiments, we verify that our model using the Gaussian kernels achieves comparable results to baseline methods, but with much fewer floating-point operations per sample up to 92% reduction from PointNet.
