Recent advances in multi-modal 3D scene understanding: A comprehensive survey and evaluation
Multi-modal 3D scene understanding has gained considerable attention due to its wide
applications in many areas, such as autonomous driving and human-computer interaction …
applications in many areas, such as autonomous driving and human-computer interaction …
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 …
Moho: Learning single-view hand-held object reconstruction with multi-view occlusion-aware supervision
Previous works concerning single-view hand-held object reconstruction typically rely on
supervision from 3D ground-truth models which are hard to collect in real world. In contrast …
supervision from 3D ground-truth models which are hard to collect in real world. In contrast …
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 …
3d weakly supervised semantic segmentation with 2d vision-language guidance
In this paper, we propose 3DSS-VLG, a weakly supervised approach for 3D S emantic S
egmentation with 2D V ision-L anguage G uidance, an alternative approach that a 3D model …
egmentation with 2D V ision-L anguage G uidance, an alternative approach that a 3D model …
Visual foundation models boost cross-modal unsupervised domain adaptation for 3d semantic segmentation
Unsupervised domain adaptation (UDA) is vital for alleviating the workload of labeling 3D
point cloud data and mitigating the absence of labels when facing a newly defined domain …
point cloud data and mitigating the absence of labels when facing a newly defined domain …
Model2scene: Learning 3d scene representation via contrastive language-cad models pre-training
Current successful methods of 3D scene perception rely on the large-scale annotated point
cloud, which is tedious and expensive to acquire. In this paper, we propose Model2Scene, a …
cloud, which is tedious and expensive to acquire. In this paper, we propose Model2Scene, a …
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 …
Domain-specific information preservation for Alzheimer's disease diagnosis with incomplete multi-modality neuroimages
H Xu, J Wang, Q Feng, Y Zhang, Z Ning - Medical Image Analysis, 2025 - Elsevier
Although multi-modality neuroimages have advanced the early diagnosis of Alzheimer's
Disease (AD), missing modality issue still poses a unique challenge in the clinical practice …
Disease (AD), missing modality issue still poses a unique challenge in the clinical practice …
Point Cloud Semantic Segmentation by Adaptively Fusing Information With Varying Distances
Z Jiang, B Yao, K Song, X Qiu… - IEEE Signal Processing …, 2024 - ieeexplore.ieee.org
Point clouds provide rich geometric representations, and point cloud semantic segmentation
is essential in many applications. As the data scale of point clouds is usually quite large …
is essential in many applications. As the data scale of point clouds is usually quite large …