SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation

@article{Tao2022SegGroupSS,
  title={SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation},
  author={An Tao and Yueqi Duan and Yi Wei and Jiwen Lu and Jie Zhou},
  journal={IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
  year={2022},
  volume={PP}
}
  • An Tao, Yueqi Duan, Jie Zhou
  • Published 18 December 2020
  • Computer Science
  • IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Most existing point cloud instance and semantic segmentation methods rely heavily on strong supervision signals, which require point-level labels for every point in the scene. However, such strong supervision suffers from large annotation costs, arousing the need to study efficient annotating. In this paper, we discover that the locations of instances matter for both instance and semantic 3D scene segmentation. By fully taking advantage of locations, we design a weakly supervised point cloud… 
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation
TLDR
This paper proposes a novel DAT model for weakly supervised point cloud segmentation, where the dual adaptive transformations are performed via an adversarial strategy at both point-level and region-level, aiming at enforcing the local and structural smoothness constraints on 3D point clouds.

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