Segment 3D point clouds using foundation models for 2D vision by with Dong
Segment 3D point clouds using foundation models for 2D vision by with Dong
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation
arXiv paper abstract https://arxiv.org/abs/2311.01989
arXiv PDF paper https://arxiv.org/pdf/2311.01989.pdf
... Segment-Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) ... foundation vision models ... capture knowledge from ... broad data ... enabling ... zero-shot segmentation ... their potential for ... 3D ... understanding ... relatively unexplored.
... present a novel framework that adapts various foundational models for the 3D point cloud segmentation task.
... making initial predictions of 2D semantic masks using different large vision models.
... then project these mask predictions from various frames of RGB-D video sequences into 3D space.
To generate robust 3D semantic pseudo labels, ... introduce a semantic label fusion strategy that effectively combines all the results via voting.
... demonstrate the effectiveness of adopting general 2D foundation models on solving 3D point cloud segmentation tasks.
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