git clone https://github.com/Pointcept/PointTransformerV3.git cp model.py ${PATH_TO_YOUR_PROJECT} cp -r serialization ${PATH_TO_YOUR_PROJECT}Align the input dictionary defined in our model file and the model will return the encoded feature of the given batch point cloud....
CODE:https://github.com/Pointcept/PointTransformerV3 一、大体内容 Point Transformer V3(PTv3)没有像V2那样在注意力机制方面寻求创新,而是专注于保持点云背景下准确性和效率之间的平衡,如下所示与上一代Point Transformer V2相比,PTv3在以下方面显示出优势:更强的性能。PTv3在各种室内和室外3D感知任务中实现了最...
Get a GitHub badge TaskDatasetModelMetric NameMetric ValueGlobal RankUses Extra Training DataBenchmark LIDAR Semantic SegmentationnuScenesPTv3 + PPTtest mIoU0.830# 1 Compare val mIoU0.812# 1 Compare Semantic SegmentationS3DISPTv3 + PPTMean IoU80.8# 1 ...
git clone https://github.com/Pointcept/PointTransformerV3.git cp model.py${PATH_TO_YOUR_PROJECT}cp -r serialization${PATH_TO_YOUR_PROJECT} Align the input dictionary defined in ourmodelfile and the model will return the encoded feature of the given batch point cloud. ...
CODE:https://github.com/Pointcept/PointTransformerV3 一、大体内容 Point Transformer V3(PTv3)没有像V2那样在注意力机制方面寻求创新,而是专注于保持点云背景下准确性和效率之间的平衡,如下所示与上一代Point Transformer V2相比,PTv3在以下方面显示出优势:更强的性能。PTv3在各种室内和室外3D感知任务中实现了最...
在本次Talk中,我们将超越3D感知与表征的范畴,从多模态数据特征提取的角度介绍我们被接收为CVPR 2024 Oral的工作Point Transformer V3 (PTv3) 的思想与设计。点云作为3D表征与感知的基础模态,其本身也是高维度稀疏非结构化数据的代表。将图像的每一个像素视为点,图像本身也可被视为点云,这佐证了这类数据结构的普...
代码链接:https://github.com/Pointcept/PointTransformerV3 网络结构 Point TransformerV3(PTv3)如下所示。与上一代PTv2相比,PTv3在以下方面显示出优势:1.更强的性能。PTv3在各种室内和室外3D感知任务中实现了最先进的结果。2.感受野较宽。得益于其简单高效,PTv3将感受野从16个点扩展到1024个点,速度更快。3....
代码链接:https://github.com/Pointcept/PointTransformerV3 作者单位:HKU SH AI Lab MPI PKU MIT 论文思路: 本文无意在注意力机制内寻求创新。相反,它侧重于利用规模(scale)的力量,克服点云处理背景下准确性和效率之间现有的权衡。从 3D 大规模表示学习的最新进展中汲取灵感,本文认识到模型性能更多地受到规模的影...
Point Transformer V3: Simpler, Faster, Stronger Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao arXiv Preprint 2023 [ Backbone ] [PTv3] - [arXiv] [Bib] [Project] →here ...
class PointTransformerV3(PointModule): def __init__( self, in_channels=6, order=("z", "z-trans", "hilbert", "hilbert-trans"), stride=(2, 2, 2, 2), enc_depths=(2, 2, 2, 6, 2), enc_channels=(32, 64, 128, 256, 512), enc_num_head=(2, 4, 8, 16, 32...