3DResT applies teacher-student semi-supervised learning to 3D referring expression segmentation, promoting high-agreement pseudo-labels into the labeled set and dynamically weighting low-agreement ones, and reports a +8.34 mIoU gain over fully supervised training at 1% labels on ScanRefer.
Lif-seg: Lidar and camera image fusion for 3d lidar semantic segmentation,
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3DResT: A Strong Baseline for Semi-Supervised 3D Referring Expression Segmentation
3DResT applies teacher-student semi-supervised learning to 3D referring expression segmentation, promoting high-agreement pseudo-labels into the labeled set and dynamically weighting low-agreement ones, and reports a +8.34 mIoU gain over fully supervised training at 1% labels on ScanRefer.