论文代码github.com/llien30/point_cloud_anomaly_detection 论文阅读 个人方法 首先附上个人对于文献阅读的一点理解,用来抛砖引玉: 粗读流程 (1)Abstract 论文提出了一种用于 3D 点云的端到端无监督异常检测框架。 这是第一个针对 3D 点云表示的一般对象处理异常检测任务的工作。 具体来说是一个基于深度变分...
它通过构建一个Anomaly-ShapeNet 3D数据集,包含正常和异常的3D点云样本,然后提出了一种名为IMRNet的自监督表示学习网络,用于检测和定位这些异常。IMRNet包含三个关键模块:Geometry-aware Point Cloud Sampling(几何感知点云采样)、Iterative Mask Reconstruction(迭代掩码重建)和Dense Feature Concatenation and Comparison(...
IMRNet包含三个关键模块:Geometry-aware Point Cloud Sampling(几何感知点云采样)、Iterative Mask Reconstruction(迭代掩码重建)和Dense Feature Concatenation and Comparison(密集特征融合与比较)。其中,几何感知点云采样模块负责根据点云的几何特征自适应地采样点,迭代掩码重建模块负责将正常点云进行掩码重建,从而学习正常...
Unlike structured 3D point cloud data, unstructured 3D point cloud data can capture the surface geometry completely. However, anomaly detection by using unstructured 3D point cloud data is more challenging, due to the nonexistence of global coordinate ordering and the difficulty of mathematically ...
Once trained, the teacher-student network pair can be leveraged jointly to fulfill 3D point cloud anomaly detection based on the calculated anomaly score. For evaluation, we compare our method against the reconstruction-based method on the ShapeNet-Part dataset. The experimental results and ablation ...
High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advances in this area, the scarcity of datasets and the lack of a systematic benchmark hinder its development. We introduce Real3D...
LiDAR Snowfall Simulation for Robust 3D Object Detection [det; Github] Text2Pos: Text-to-Point-Cloud Cross-Modal Localization [localization; PyTorch] Stratified Transformer for 3D Point Cloud Segmentation [seg; PyTorch] REGTR: End-to-end Point Cloud Correspondences with Transformers [registration; Py...
这个ufo可不是外星人那个啊 是 Unidentified Foreground Object Detection in 3D Point Cloud 在这篇论文中提出了一个关于在3D点云中检测未知前景物体(UFO)的新问题,这是自动驾驶在野外中的一个关键技术。UFO检测具有挑战性,因为现有的3D目标检测器在3D定位和Out-of-Distribution(OOD)检测方面都遇到了极其困难的挑战...
Currently, 2D anomaly detection has demonstrated outstanding performance. However, 2D images limit the improvement of anomaly detection accuracy without ut
* 题目: End-to-End 3D Object Detection using LiDAR Point Cloud* PDF: arxiv.org/abs/2312.1537* 作者: Gaurav Raut,Advait Patole 三维视觉-分割 3篇 * 题目: 2D-Guided 3D Gaussian Segmentation* PDF: arxiv.org/abs/2312.1604* 作者: Kun Lan,Haoran Li,Haolin Shi,Wenjun Wu,Yong Liao,Lin Wang...