deep-learningtransformerspytorch3dvisionpoint-cloud-completioniccv2021tpami2023 UpdatedJul 22, 2024 Python A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion diffusion-modelpoint-cloud-completion UpdatedApr 14, 2024 Python ...
A point cloud is a set of data points in space. The points represent a 3D shape or object. Each point has its set of X, Y and Z coordinates. Here are 1,725 public repositories matching this topic... Language:All Sort:Most stars ...
Cascade Refinement Network for Point Cloud Completionarxiv.org/abs/2004.03327 cascaded-point-completiongithub.com/xiaogangw/cascaded-point-completion
[2]Haozhe Xie, Hongxun Yao, Shangchen Zhou, Jiageng Mao,Shengping Zhang, and Wenxiu Sun. GRNet: Gridding Residual Network for Dense PointCloud Completion. ECCV, pages 365–381, 2020.[3]Wentao Yuan, Tejas Khot, David Held, Christoph Mertz,and Martial Hebert. PCN: Point Completion Network. ...
· 项目主页:pointcloud-c.github.io/ · 开源代码:github.com/ldkong1205/P 一句话总结 首个用于评测点云识别模型鲁棒性的基线,包括ModelNet-C和ShapeNet-C两个子集;贴近真实世界的设计,涵盖物体(object)、传感器(sensor)和处理(processing)等阶段中的corruption情形;为基于监督学习、自监督预训练和数据增强等的点...
代码地址:https://github.com/zztianzz/PF-Net-Point-Fractal-Network.git 点云补全(Point Cloud Completion)用于修补有所缺失的点云(Point Cloud),从缺失点云出发估计完整点云,从而获得更高质量的点云。点云有助于用较小的数据量描述三维物体,在三维物体的检测识别领域应用广泛。在 PointNet[1] 和 PointNet++...
As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning,
Point cloud completion estimates the complete shape given incomplete point cloud, which is a crucial task as the raw point cloud measurements suffer from missing data. Most of previous methods for point cloud completion share the encoder-decoder structure, where the encoder projects the raw point ...
PoinTr is a transformer-based model for point cloud completion. By representing the point cloud as a set of unordered groups of points with position embeddings, we convert the point cloud to a sequence of point proxies and employ a transformer encoder-decoder architecture for generation. We also...
2. SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer (ICCV 2021, Oral) Most existing point cloud completion methods suffer from the discrete nature of point clouds and the unstructured prediction of points in local regions, which makes it difficult to reveal...