[Keyword] Point Cloud Classification, Robustness, Corruption Taxonomy 这篇文章对真实世界的点云损坏进行了归类,建立了对应benchmark,并对常见的点云分类模型进行了鲁棒性的研究。文章从三个不同层面给出了鲁棒点云分类模型的设计建议,并提出了一个鲁棒的点云分类模型RPC(Robust Point Cloud Classifier)。 先放...
对点云所对应地物类别进行判定的过程。 来源:《测绘学名词》(第三版),科学出版社
PointCloud-C中为点云分类(classification)任务所设计的corruption测试集ModelNet-C所下图所示。 PointCloud中点云分类(classification)任务子集ModelNet-C的样例示意 类似地,PointCloud-C中为点云部件分割(part segmentation)任务所设计的corruption测试集ShapeNet-C所下图所示。 PointCloud中点云部件分割(part segmentation)...
To prove this, we introduce\nScanObjectNN, a new real-world point cloud object dataset based on scanned\nindoor scene data. From our comprehensive benchmark, we show that our dataset\nposes great challenges to existing point cloud classification techniques as\nobjects from real-world scans are...
1. (2023年新疆大学、中科院等点云分类最新综述) Deep learning-based 3D point cloud classification: A systematic survey and outlook(2) 2. (2022 IVC 行人再识别综述)Deep learning-based person re-identification methods: A survey and outlook of recent works(2) 3. 给刚刚进入实验室(或入门深度学...
fine-grained point cloud classification fine-grained point cloud classification的意思是:细粒度点云分类。是一个研究领域,主要关注于对点云数据进行精细的分类。点云数据是一种用于表示三维空间中物体表面点集的数据结构,广泛应用于计算机视觉、机器人技术、地理信息系统等领域。
One of the seminal deep learning techniques for point cloud classification is PointNet [1]. This example trains a PointNet classifier on the Sydney Urban Objects data set created by the University of Sydney [2]. This data set provides a collection of point cloud data acquired from an urban ...
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-Training CLIP2Point:通过图像深度预训练将 CLIP 传输到点云分类 ICCV 2023 摘要 由于训练数据有限,3D 视觉和语言的预训练仍在开发中。最近的工作尝试将视觉语言(V-L)预训练方法转移到 3D 视觉。然而,3D 和图像之间的领域差距尚未解...
Yes I did try using Infraworks Point cloud classification, But it took a lot of time to classify and the output was very Jaggy... Not very smooth. Looking for a more efficient solution.:). I like the way Infraworks have the Point cloud classification built into the sandbox but i...
Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification 学习笔记 1.该论文的贡献: 提出了一种新的、更难以察觉(imperceptible)、更可移植(transfer)的对3D点云classification攻击手段,同时也提出一种新的3D点云classification防御手段。