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PCT: Point cloud transformer [PDF]

Computational Visual Media, 2021
The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image ...
Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R. Martin, Jun-Xiong Cai, Shi-Min Hu   +5 more
openaire   +7 more sources

AIRBORNE LIDAR POINT CLOUD CLASSIFICATION FUSION WITH DIM POINT CLOUD [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
Airborne Light Detection And Ranging (LiDAR) point clouds and images data fusion have been widely studied. However, with recent developments in photogrammetric technology, images can now provide dense image matching (DIM) point clouds.
M. Zhou, Z. Kang, Z. Wang, M. Kong
doaj   +3 more sources

Adaptive Clustering for Point Cloud

Sensors
The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to ...
Zitao Lin, Chuanli Kang, Siyi Wu, Xuanhao Li, Lei Cai, Dan Zhang, Shiwei Wang   +6 more
doaj   +3 more sources

Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests [PDF]

Remote Sensing, 2017
This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF ...
Huan Ni, Xiangguo Lin, Jixian Zhang
doaj   +3 more sources

Masked Autoencoders for Point Cloud Self-supervised Learning [PDF]

European Conference on Computer Vision, 2022
As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning,
Yatian Pang, Wenxiao Wang, Francis E. H. Tay, W. Liu, Yonghong Tian, Liuliang Yuan   +5 more
semanticscholar   +1 more source

Geometric Transformer for Fast and Robust Point Cloud Registration [PDF]

Computer Vision and Pattern Recognition, 2022
We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult in low-overlap scenarios, showing great potential in registration. They
Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Kaiping Xu   +5 more
semanticscholar   +1 more source

Stratified Transformer for 3D Point Cloud Segmentation [PDF]

Computer Vision and Pattern Recognition, 2022
3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies.
Xin Lai, Jianhui Liu, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia   +7 more
semanticscholar   +1 more source

Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling [PDF]

Computer Vision and Pattern Recognition, 2021
We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT [8] to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically, we first divide a
Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, Jiwen Lu   +5 more
semanticscholar   +1 more source

CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding [PDF]

Computer Vision and Pattern Recognition, 2022
Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds.
Mohamed Afham, Isuru Dissanayake, Dinithi Dissanayake, Amaya Dharmasiri, Kanchana Thilakarathna, R. Rodrigo   +5 more
semanticscholar   +1 more source

Diffusion Probabilistic Models for 3D Point Cloud Generation [PDF]

Computer Vision and Pattern Recognition, 2021
We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation.
Shitong Luo, Wei Hu
semanticscholar   +1 more source