POINT cloudARTIFICIAL intelligenceCULTURAL propertyDeep Learning has been pivotal in many real-world applications (e.g., autonomous driving, medicine and retail). With the wide availability of consumer-grade depth sensors, acquiring 3D data has become more...
为 层模型做出的结果与实际label之间的交叉熵,即仍然要保证本身的classification结果正确性。
randomizedsmoothing 只能保证对于原始数据修改的attack(例如加adversarial perturbation), 不能保证deletion, addition attack, which is pretty common in Point Cloud domian。 核心算法:基于subsampling的aggregation prediction。 随机采样从T个点里采样k个点得到N个subpoint cloud 2. 得到每个subpoint cloud的prediction ...
http://bing.comLepard: Learning Partial Point Cloud Matching in Rigid and Deformable Scenes | CVPR 2022CVPR 2022论文列表及代码:https://github.com/gbstack/CVPR-2022-papers字幕版之后会放出,敬请持续关注欢迎加入人工智能机器学习群:55691094
First, the point cloud is voxelized to reduce the number of points needed to be processed sequentially. Next, descriptive voxel attributes are assigned to aid in further classification. These attributes describe the point distribution within each voxel and the voxel’s geo-location. These include 5...
Point Cloud Transformer 用的是global attention，是用了四层的Attention Feature组合形成（体感上有点像...
Deep learning-based 3D point cloud classification: A systematic survey and outlook HuangZhang, ...XiaoBai, inDisplays, 2023 3.2Application of point cloud In the processing process of thepoint cloud, the same kind of point cloud data not only has similar reflection intensity, color, and other in...
3-D Point Cloud Classification 3-D Point Cloud Segmentation @article{zhang2024point,title={Point Cloud Mamba: Point Cloud Learning via State Space Model},author={Zhang, Tao and Li, Xiangtai and Yuan, Haobo and Ji, Shunping and Yan, Shuicheng},journal={arXiv preprint arXiv:2403.00762},year...
The point cloud classification model can be trained using either a CUDA-capable NVIDIA graphics card or the CPU. Using the GPU is typically faster than using the CPU. Use the CPU only if no GPU is available. When using the CPU for training, start by using the RandLA-Net architecture ...
Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning...