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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 …
Point cloud pre-training with natural 3d structures
The construction of 3D point cloud datasets requires a great deal of human effort. Therefore,
constructing a largescale 3D point clouds dataset is difficult. In order to remedy this issue …
constructing a largescale 3D point clouds dataset is difficult. In order to remedy this issue …
[HTML][HTML] An in-depth analysis of domain adaptation in computer and robotic vision
This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …
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 …
Domain adaptive sampling for cross-domain point cloud recognition
Point cloud recognition has recently gained increasing research interest due to the huge
potential in real-world applications such as autonomous driving, robotics, etc. However, the …
potential in real-world applications such as autonomous driving, robotics, etc. However, the …
Category-level adversaries for outdoor LiDAR point clouds cross-domain semantic segmentation
Unsupervised domain adaptation (UDA) is a low-cost way to deal with the lack of
annotations in a new domain. For outdoor point clouds in urban transportation scenes, the …
annotations in a new domain. For outdoor point clouds in urban transportation scenes, the …
Saluda: Surface-based automotive lidar unsupervised domain adaptation
Learning models on one labeled dataset that generalize well on another domain is a difficult
task, as several shifts might happen between the data domains. This is notably the case for …
task, as several shifts might happen between the data domains. This is notably the case for …
GPDAN: Grasp pose domain adaptation network for sim-to-real 6-DoF object gras**
In this letter, we propose a novel Grasp Pose Domain Adaptation Network (GPDAN) to
achieve sim-to-real domain adaptation for 6-DoF grasp pose detection. The main task of …
achieve sim-to-real domain adaptation for 6-DoF grasp pose detection. The main task of …
Prototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation
Unsupervised domain adaptation (UDA) segmentation aims to leverage labeled source data
to make accurate predictions on unlabeled target data. The key is to make the segmentation …
to make accurate predictions on unlabeled target data. The key is to make the segmentation …
Sug: Single-dataset unified generalization for 3d point cloud classification
Although Domain Generalization (DG) problem has been fast-growing in the 2D image
tasks, its exploration on 3D point cloud data is still insufficient and challenged by more …
tasks, its exploration on 3D point cloud data is still insufficient and challenged by more …