\* indicates equal contribution, † indicates the corresponding author.

Proposed an instance-level strong augmentation strategy for semi-supervised 3D object detection to fully exploit instance-specific information for accurate object detection in 3D environments.

Proposed a probabilistic decision adaptive thresholding method for semi-supervised 3D object detection, which dynamically adjusts thresholds based on learned states at scene, batch, and class levels, effectively improving pseudo-label quality and detection performance on ScanNet and SUN RGB-D.

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