Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey
The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …
deep learning (DL). However, the latter faces various issues, including the lack of data or …
Domain adaptation on point clouds via geometry-aware implicits
As a popular geometric representation, point clouds have attracted much attention in 3D
vision, leading to many applications in autonomous driving and robotics. One important yet …
vision, leading to many applications in autonomous driving and robotics. One important yet …
A survey of label-efficient deep learning for 3D point clouds
In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …
learning. However, collecting large-scale precisely-annotated point clouds is extremely …
Anatomy-guided domain adaptation for 3D in-bed human pose estimation
A Bigalke, L Hansen, J Diesel, C Hennigs… - Medical Image …, 2023 - Elsevier
Abstract 3D human pose estimation is a key component of clinical monitoring systems. The
clinical applicability of deep pose estimation models, however, is limited by their poor …
clinical applicability of deep pose estimation models, however, is limited by their poor …
Learning generalizable part-based feature representation for 3d point clouds
Deep networks on 3D point clouds have achieved remarkable success in 3D classification,
while they are vulnerable to geometry variations caused by inconsistent data acquisition …
while they are vulnerable to geometry variations caused by inconsistent data acquisition …
Self-distillation for unsupervised 3D domain adaptation
Point cloud classification is a popular task in 3D vision. However, previous works, usually
assume that point clouds at test time are obtained with the same procedure or sensor as …
assume that point clouds at test time are obtained with the same procedure or sensor as …
Self-supervised boundary point prediction task for point cloud domain adaptation
J Chen, Y Zhang, K Huang, F Ma… - IEEE Robotics and …, 2023 - ieeexplore.ieee.org
Unsupervised domain adaptation (UDA) could significantly improve the cross-domain
performance of current supervised 3D deep learning methods and have a widespread …
performance of current supervised 3D deep learning methods and have a widespread …
Scoda: Domain adaptive shape completion for real scans
Abstract 3D shape completion from point clouds is a challenging task, especially from scans
of real-world objects. Considering the paucity of 3D shape ground truths for real scans …
of real-world objects. Considering the paucity of 3D shape ground truths for real scans …
Masked local-global representation learning for 3d point cloud domain adaptation
B **ng, X Ying, R Wang - 2024 IEEE International Conference …, 2024 - ieeexplore.ieee.org
Point cloud is a popular and widely used geometric representation, which has attracted
significant attention in 3D vision. However, the geometric variability of point cloud …
significant attention in 3D vision. However, the geometric variability of point cloud …
3dos: Towards 3d open set learning-benchmarking and understanding semantic novelty detection on point clouds
In recent years there has been significant progress in the field of 3D learning on
classification, detection and segmentation problems. The vast majority of the existing studies …
classification, detection and segmentation problems. The vast majority of the existing studies …