Point cloud shape completion is a challenging problem in 3D vision and robotics. Existing learning-based frameworks leverage encoder-decoder architectures to recover the complete shape from a highly encoded global feature vector. Though the global feature can approximately represent the overall shape of ...
High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization 题目:利用潜在优化方法完成点云的高保真语义的形状补全 摘要 在3D计算机视觉中语义形状补全是一个具有挑战性的问题,这个任务是使用一个部分3D形状作为输入产生一个完整的3D形状。作者提出了一个基于学习的方法通过生成模型和潜在的流形优...
论文笔记 - Cascaded Refinement Network for Point Cloud Completion - 2020 CVPR introduction部分的研究背景。作者团队首先提出了三维点云的生成存在着稀疏、不完整、不规则的特点,学习到准确的点云的特征以及多样的分布是一件困难的事,也因此增加了生成完整、稠密三维物体形状的难度。 文章提到了相关工作中的3D生成...
所以,在拼接之前对P_{input}使用farthest point sampling(FPS)算法进行采样,采样后的shape为N_{c} \times 3,紧接着对采样后的点进行Mirror操作(文中对Mirror操作是这样解释的,我们的mirror operation可以看作是对缺失点的初始化,以及通过整体优化生成合理位置的点,在实现过程中,以xy平面为对称平面,将z方向上的值...
点云补洞算法+代码(Shape Controllable geometry completion for point cloud models) 导读 : 今天为大家带来针对点云的空洞填补,针对点云的补全和补洞算法是比较少见的,大多数算法都只是针对网格的补洞。而针对点云的补洞在三维重建中能够很有效的应用,特别是如泊松重建中因遮挡导致的点云稀疏或缺失的区域,但点云...
Point cloud completion is the task of producing a complete 3D shape given an input of a partial point cloud. It has become a vital process in 3D computer graphics, vision and applications such as autonomous driving, robotics, and augmented reality. These applications often rely on the presence...
6. Shape-Oriented Convolution Neural Network for Point Cloud Analysis 会议:AAAI 2020. AAAI Technical Track: Vision. 作者:Chaoyi Zhang, Yang Song, Lina Yao, Weidong Cai 链接:https://aaai.org/ojs/index.php/AAAI/article/view/6972/6826
Point-cloud data collected in real-world applications are often incomplete, because objects are being observed from specific viewpoints, which only capture one perspective. Data can also be incomplete due to occlusion and low-resolution sampling. Existing approaches to completion rely on training models...
This repository contains the official implementation for "Cross-modal Learning for Image-Guided Point Cloud Shape Completion" (NeurIPS 2022)paper Introduction In this paper we explore the recent topic of point cloud completion, guided by an auxiliary image. We show how it is possible to effectively...
由于CD(Chamfer Distance)损失函数不能保证网络预测的点云遵循物体的几何布局,并且使得网络倾向输出一个最小化距离的平均shape;另一方面,通过映射的方式需要额外的相机参数,在大多数场景下进行评估都面临着挑战。为了解决点云数据无序性的问题,本文设计了一个Gridding Loss——它通过使用提出的Gridding Layer在常规的3D网...