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[HTML][HTML] Self-supervised learning for point cloud data: A survey
Abstract 3D point clouds are a crucial type of data collected by LiDAR sensors and widely
used in transportation applications due to its concise descriptions and accurate localization …
used in transportation applications due to its concise descriptions and accurate localization …
Self-supervised learning for pre-training 3d point clouds: A survey
Point cloud data has been extensively studied due to its compact form and flexibility in
representing complex 3D structures. The ability of point cloud data to accurately capture and …
representing complex 3D structures. The ability of point cloud data to accurately capture and …
Masked surfel prediction for self-supervised point cloud learning
Masked auto-encoding is a popular and effective self-supervised learning approach to point
cloud learning. However, most of the existing methods reconstruct only the masked points …
cloud learning. However, most of the existing methods reconstruct only the masked points …
3D shape contrastive representation learning with adversarial examples
Current supervised methods for 3D shape representation learning have achieved satisfying
performance, yet require extensive human-labeled datasets. Unsupervised learning-based …
performance, yet require extensive human-labeled datasets. Unsupervised learning-based …
Point-dae: Denoising autoencoders for self-supervised point cloud learning
Masked autoencoder has demonstrated its effectiveness in self-supervised point cloud
learning. Considering that masking is a kind of corruption, in this work we explore a more …
learning. Considering that masking is a kind of corruption, in this work we explore a more …
Towards robustness and generalization of point cloud representation: A geometry coding method and a large-scale object-level dataset
Robustness and generalization are two challenging problems for learning point cloud
representation. To tackle these problems, we first design a novel geometry coding model …
representation. To tackle these problems, we first design a novel geometry coding model …
Self-Supervised Pretraining Framework for Extracting Global Structures From Building Point Clouds via Completion
H Yang, R Wang - IEEE Transactions on Geoscience and …, 2024 - ieeexplore.ieee.org
The exterior structural information of buildings are crucial for advancing smart city initiatives
and reconstructing 3-D edifices. However, practical obstacles, such as sparse or incomplete …
and reconstructing 3-D edifices. However, practical obstacles, such as sparse or incomplete …
Point‐AGM: Attention Guided Masked Auto‐Encoder for Joint Self‐supervised Learning on Point Clouds
J Liu, M Yang, Y Tian, Y Li, D Song… - Computer Graphics …, 2024 - Wiley Online Library
Masked point modeling (MPM) has gained considerable attention in self‐supervised
learning for 3D point clouds. While existing self‐supervised methods have progressed in …
learning for 3D point clouds. While existing self‐supervised methods have progressed in …
PointAS: an attention based sampling neural network for visual perception
B Qiu, S Li, L Wang - Frontiers in Computational Neuroscience, 2024 - frontiersin.org
Harnessing the remarkable ability of the human brain to recognize and process complex
data is a significant challenge for researchers, particularly in the domain of point cloud …
data is a significant challenge for researchers, particularly in the domain of point cloud …
PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification
Q Zheng, C Zhang, J Sun - arxiv preprint arxiv:2409.02007, 2024 - arxiv.org
Advances in self-supervised learning are essential for enhancing feature extraction and
understanding in point cloud processing. This paper introduces PMT-MAE (Point MLP …
understanding in point cloud processing. This paper introduces PMT-MAE (Point MLP …