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Found 3 papers, 1 papers with code
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?
Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training.
Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection
State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations.
SS3D: Sparsely-Supervised 3D Object Detection From Point Cloud
In the meantime, we design a reliable background mining module and a point cloud filling data augmentation strategy to generate the confident data for iteratively learning with reliable supervision.
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