Real-time 3D graphics—everyone loves them these days. Computing power has finally evolved to the point where it’s feasible to render something as complex as the human face interactively, with enough structural and surface detail that it doesn’t look
A Useful toolkit for all RObocuppers in Soccer 3d simulation ability to controll game status and drop ball and agents position is a point of this program
In this paper we present a method for fast surface reconstruction from large noisy datasets. Given an unorganized 3D point cloud, our algorithm recreates the underlying surface’s geometrical properties using data resampling and a robust triangulatio
PCD (Point Cloud Data) file format is used inside Point Cloud Library (PCL). The PCD file format is not meant to reinvent the wheel, but rather to complement existing file formats that for one reason or another did not/do not support some of the ext
AppWizard has created this Terrain3DTest application for you. This application not only demonstrates the basics of using the Microsoft Foundation classes but is also a starting point for writing your application. This file contains a summary of what
Abstract— This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implemen
can be used in various application like develop 3D models of objects or build 3D world maps for SLAM(Simultaneous Localization And Mapping).Acquiring Kinect Data (RGB + Depth + Point Cloud)
Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos), many other data sources are inherently sparse. Examples include 3D
Point2Sequence：使用基于注意力的序列到序列网络学习3D点云的形状表示
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引文
如果您发现我们的工作对您的研究有用，请考虑引用：
inproceedings{liu2019point2sequence,
title={Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Netwo
