3D point cloud denoising is a fundamental task in a geometry-processing pipeline, where feature preservation is essential for various applications. The literature presents several methods to overcome the denoising problem; however, most of them focus on denoising smooth surfaces and not on handling ...
3D point cloud compression 3D point cloud denoising 3D point cloud registration 3D point cloud downsampling and upsampling 点云学习(3D point cloud recognition and segmentation) 2、3D点云的处理&学习 3D点云的属性 Real-time LiDAR sweeps point-cloud maps 矩阵表示(由点集转换成矩阵表示) 3D空间的离散化...
文章链接:(https://openaccess.thecvf.com/content/CVPR2024/html/Shin_Spherical_Mask_Coarse-to-Fine_3D_Point_Cloud_Instance_Segmentation_with_Spherical_CVPR_2024_paper.html) TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process 文章解读:http://www.studyai.com/xues...
论文名称:Differentiable Manifold Reconstruction for Point Cloud Denoising 原文作者:Shitong Luo 内容提要 3D点云由于采集设备的固有局限性,经常受到噪声的干扰,阻碍了3D点云的表面重建、绘制等后续工作。以往的工作主要是从下曲面推断出有噪点的位移,但没有明确地指定去噪点来恢复曲面,可能导致去噪结果不理想。为此,本...
P2P-Bridge: Diffusion Bridges for3D Point Cloud Denoising In this work, we address the task of point cloud denoising using a novel framework adapting Diffusion Schrdinger bridges to unstructured data like point se... M Vogel,K Tateno,M Pollefeys,... - European Conference on Computer Vision 被...
3D point clouds denoisingOutliers removingNowadays, robots are able to carry out a complex series of actions, to take decisions, to interact with their environment and generally to perform plausible reactions. Robots' visual ability plays an important role to their behavior, helping them to ...
PointCleanNet提出了一种基于数据驱动的方法去消除错误点减少噪声 PCPNet首先对异常值进行分类并丢弃它们,然后估计一个将噪声投影到原始表面的修正投影 Total Denoising,在不需要额外数据的情况下实现了非监督降噪对点云数据 临界点层(CPL)在保留重要点的同时学会减少点的数量。这一层是确定性的,不确定顺序的,并且通过...
Masked Autoencoders for Point Cloud Self-supervised Learning [self-supervised; PyTorch] CVFNet: Real-time 3D Object Detection by Learning Cross View Features [det] PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows [denoising] PETR: Position Embedding Transformation for Multi-View 3D...
PointCleanNet提出了一种基于数据驱动的方法去消除错误点减少噪声 PCPNet首先对异常值进行分类并丢弃它们,然后估计一个将噪声投影到原始表面的修正投影 Total Denoising,在不需要额外数据的情况下实现了非监督降噪对点云数据 临界点层(CPL)在保留重要点的同时学会减少点的数量。这一层是确定性的,不确定顺序的,并且通过...
Point-E [NJD22] Diffusion Point Point Cloud - $3\mathrm{D}$ Text 3DGen [GXN23] Diffusion Tri-plane Mesh - $3\mathrm{D}$ Text/Image DreamFusion [PJBM23] Diffusion - NeRF Volume Rendering SDS Text Make-It-3D [TWZ23] Diffusion - Point Cloud Network Rendering SDS Image Zero-1-to-3...