pointcloud-sotagithub.com/yeyan00/pointcloud-sota 点云深度学习的任务主要集中在以下几个方面:分类(Classification)、分割(Segmentation)、目标检测(Object Detection)、实例分割(Panoptic Segmentation)、配准(Registration)、点云重构(Reconstruction)。 点云深度学习方法在论文(Deep Learning for 3D Point Clouds: ...
由于XNA是面向开发小游戏的,做3D图形研究非常不适合,一来速度受到限制,二来没有GPU的接口,三来由于其基于C#.net平台,无法利用已有的3D图形开源资源(因为3D绝大部分学术资源都是用C++写的,像cgal,meshlab,vtk,还有很多数学库等等),而且还只有我自己一个人做,这样意味着所有琐碎的东西你都要自己写,根本没有足够精...
et al. Point Cloud 3D Weldment Reconstruction and Welding Feature Extraction for Robotic Multi-bead Arc Weld Cladding Path Planning. Int. J. Precis. Eng. Manuf. 25, 1027–1041 (2024). https://doi.org/10.1007/s12541-024-00964-2 Download citation Received29 March 2023 Revised15 January 2024...
受bert启发,设计了a Masked Point Modeling (MPM) task 预训练 point cloud Transformers。首先将点云分割为several local point patches,设计了一个带有discrete Variational AutoEncoder (dVAE)的a point cloud Tokenizer——生成discrete point tokens包含了局部信息。然后,随机mask out一些输入点云的patches,feed them...
3D point cloudReconstructing semantically rich building information model (BIM) from 2D images or 3D point clouds represents a research realm that is gaining increasing popularity in architecture, engineering, and construction. Researchers have found that architectural design knowledge, such as symmetry, ...
Lai, L., Chen, J., & Wu, Q. (2023). Zero-Shot Single-View Point Cloud Reconstruction via Cross-Category Knowledge Transferring. IEEE Transactions on Multimedia. 摘要:单视点云重建的目的是在给定从任意视点拍摄的一张 2D 图像的情况下生成对象的 3D 点云。以前的大多数工作都假设所有测试类别在训练...
Generating a more realistic 3D reconstruction point cloud is an ill-posed problem. It is a challenging task to infer 3D shape from a single image. In this
With the rapid development of various three dimensional scanning devices, the 3D point cloud data of many objects in the real world can be easily obtained. Therefore, research on 3D reconstruction technology based on point cloud has important practical significance. In this paper, first the research...
根据论文《DiffPoint: Single and Multi-view Point Cloud Reconstruction with ViT Based Diffusion Model》的介绍,DiffPoint结合了视觉变换器(Vision Transformer, ViT)和扩散模型的优势,提出了一种新的架构,用于从单个或多个2D图像中重建3D点云。这种方法的核心在于它能够处理点云的局部细节和全局结构,从而在3D重建...
文章链接:(https://openaccess.thecvf.com/content/CVPR2024/html/Xu_PDF_A_Probability-Driven_Framework_for_Open_World_3D_Point_Cloud_CVPR_2024_paper.html) L4D-Track: Language-to-4D Modeling Towards 6-DoF Tracking and Shape Reconstruction in 3D Point Cloud Stream ...