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 …
BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation
Abstract Cross-modal Unsupervised Domain Adaptation (UDA) aims to exploit the
complementarity of 2D-3D data to overcome the lack of annotation in a new domain …
complementarity of 2D-3D data to overcome the lack of annotation in a new domain …
Learning to adapt sam for segmenting cross-domain point clouds
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable
challenge, primarily steming from the sparse and unordered nature of point clouds …
challenge, primarily steming from the sparse and unordered nature of point clouds …
Reliable spatial-temporal voxels for multi-modal test-time adaptation
Multi-modal test-time adaptation (MM-TTA) is proposed to adapt models to an unlabeled
target domain by leveraging the complementary multi-modal inputs in an online manner …
target domain by leveraging the complementary multi-modal inputs in an online manner …
Mopa: Multi-modal prior aided domain adaptation for 3d semantic segmentation
Multi-modal unsupervised domain adaptation (MM-UDA) for 3D semantic segmentation is a
practical solution to embed semantic understanding in autonomous systems without …
practical solution to embed semantic understanding in autonomous systems without …
Unsupervised domain adaptation for video object grounding with cascaded debiasing learning
This paper addresses the Unsupervised Domain Adaptation (UDA) for the dense frame
prediction task-Video Object Grounding (VOG). This investigation springs from the …
prediction task-Video Object Grounding (VOG). This investigation springs from the …
Clip2uda: Making frozen clip reward unsupervised domain adaptation in 3d semantic segmentation
Multi-modal Unsupervised Domain Adaptation (MM-UDA) for large-scale 3D semantic
segmentation involves adapting 2D and 3D models to a target domain without labels, which …
segmentation involves adapting 2D and 3D models to a target domain without labels, which …
Open-vocabulary affordance detection using knowledge distillation and text-point correlation
Affordance detection presents intricate challenges and has a wide range of robotic
applications. Previous works have faced limitations such as the complexities of 3D object …
applications. Previous works have faced limitations such as the complexities of 3D object …
Sam-guided unsupervised domain adaptation for 3d segmentation
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable
challenge, primarily stemming from the sparse and unordered nature of point cloud data …
challenge, primarily stemming from the sparse and unordered nature of point cloud data …
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation
Due to privacy or patent concerns, a growing number of large models are released without
granting access to their training data, making transferring their knowledge inefficient and …
granting access to their training data, making transferring their knowledge inefficient and …