参考 MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions FalconNet: Factorization for the Light-weight Co...
Deep learning with 3D data such as reconstructed pointclouds and CAD models has received great research interestsrecently. However, the capability of using point cloudswith convolutional neural network has been so far not fullyexplored. In this paper, we present a convolutional neuralnetwork for seman...
Convolutional Neural Networks 9.5、Convolutional Neural Networks卷积神经网络 卷积神经网络是人工神经网络的一种,已成为当前语音分析和图像识别领域的研究热点。它的权值共享网络结构使之更类似于生物神经网络,降低了网络模型的复杂度,减少了权值的数量。该优点在网络的输入是多维图像时表现的更为明显,使图像可以直接作为...
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationsarxiv.org MobileNet的核心就是Depthwise separable convolution(深度可分离卷积),它将一般的卷积过程分为了depthwise convolution(逐深度卷积)和pointwise convolution(逐点卷积),在损失一点精度的情况下,计算量大幅下降,速度更快,模型更...
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationsarxiv.org MobileNet的核心就是Depthwise separable convolution(深度可分离卷积),它将一般的卷积过程分为了depthwise convolution(逐深度卷积)和pointwise convolution(逐点卷积),在损失一点精度的情况下,计算量大幅下降,速度更快,模型更...
Pointwise Convolutional Neural Networks 本文提出了一种用于三维点云语义分割和目标识别的卷积神经网络。网络的核心是逐点卷积,这是一种新的卷积算子,可以应用于点云的每个点。作者使用全卷积网络设计,虽然实现起来出奇地简单,但在语义分割和对象识别任务中都能产生有竞争力的准确性。作者表明,在进行对象分类的特征学习...
Pointwise Convolutional Neural Networks 论文翻译 原文作者:Binh-Son Hua、Minh-Khoi Tran、Sai-Kit Yeung。 文章地址:https://arxiv.org/abs/1712.05245 github项目地址:https://github.com/scenenn/pointwise 在本文中,我们提出了一种用于3D点云的语义分割和目标识别的卷积神经网络。 我们网络的核心是逐点卷积,这...
卷积神经网络在图像处理中的地位已然毋庸置疑。卷积运算具备强大的特征提取能力、相比全连接又消耗更少的参数,应用在图像这样的二维结构数据中有着先天优势。然而受限于目前移动端设备硬件条件,显著降低神经网络的运算量依旧是网络结构优化的目标之一。本文所述的Separable Convolution就是降低卷积运算参数量的一种典型方法。
This is the release of the code for the paper `Pointwise Convolutional Neural Networks' in CVPR 2018. Usage The code is tested in latest Ubuntu 18.04 LTS with CUDA 9.2 and Tensorflow 1.9. First, we need to compile the convolution operator as follows: ...
With the rapid progress of deep convolutional neural networks, in almost all robotic applications, the availability of 3D point clouds improves the accuracy of 3D semantic segmentation methods. Rendering of these irregular, unstructured, and unordered 3D points to 2D images from multiple viewpoints impo...