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: ...
通过对不同点云学习任务的大量实验表明,所提出的 PointRWKV 优于基于 transformer 和 mamba 的同类网络,同时显著节省了约 42% 的 FLOPs,展示了构建基础 3D 点云表征学习模型的优越性。 论文标题: PointRWKV: Efficient RWKV-Like ...
2、 FlowNet3D++: Geometric losses for deep scene flow estimation 3、 HPLFlowNet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds 4、 PointRNN: Point recurrent neural network for moving point cloud processing 5、 MeteorNet: Deep learning on dynamic 3D...
FlowNet3D++: Geometric lossesfor deep scene flow estimation HPLFlowNet: Hierarchicalpermutohedral lattice flownet for scene flow estimation PointRNN: Point recurrentneural network for moving point cloud processing MeteorNet: Deep learningon dynamic 3D point cloud ...
CreativeAI: Deep Learning for Graphics Datasets To see a survey of RGBD datasets, check out Michael Firman's collection as well as the associated paper, RGBD Datasets: Past, Present and Future. Point Cloud Library also has a good dataset catalogue. 3D Models Princeton Shape Benchmark (2003) ...
Learning-based 3D Point Cloud Enhancement: from Static to Dynamic 3D point clouds are widely used in immersive telepresence, cultural heritage reconstruction, geophysical information systems, autonomous driving, and virtual/augmented reality. Despite rapid development in 3D sensing technology, acquiring 3D ...
随着基于广泛数据训练的大模型兴起,上下文学习(In-Context Learning)已成为一种新的学习范式,在自然语言处理(NLP)和计算机视觉(CV)任务中表现出了巨大的潜力。与此同时,在3D点云(Point Cloud)领域中,上下文学习在很大程度上仍未得到探索。尽管掩码建模(Masked Modeling)训练策略已经成功应用于2D视觉中的上下文学习,但将...
Qi, Charles Ruizhongtai, et al. "Pointnet++: Deep hierarchical feature learning on point sets in a metric space."Advances in neural information processing systems. 2017. [paper] Zhao, Hengshuang, et al. "PointWeb: Enhancing local neighborhood features for point cloud processing."Proceedings of the...
本文的结构如下。 第2节回顾了3D形状分类的方法。 第3节概述了3D对象检测和跟踪的现有方法。 第4节概述了点云分割方法,包括语义分割,实例分割和零件分割。 最后,第5节总结了论文。 我们还在以下网址上提供了定期更新的项目页面:https://github.com/QingyongHu/SoTA-Point-Cloud。
PointRNN: Point recurrentneural network for moving point cloud processing MeteorNet: Deep learning on dynamic 3D point cloud sequences Just go with the flow:Self-supervised scene flow estimation 3D点云分割 三维点云分割需要了解全局几何结构和每个点的细粒度细节。根据分割粒度,三维点云分割方法可分为三类...