# Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection Source: [Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:19:28.634Z Authors: Cheng-Ju Ho, Chen-Hsuan Tai, Yi-Hsuan Tsai, Yen-Yu Lin, Ming-Hsuan Yang Published: 2022-12-19T06:56:14.000Z ## Abstract Semi-supervised object detection is important for 3D scene understanding because obtaining large-scale 3D bounding box annotations on point clouds is time-consuming and labor-intensive. Existing semi-supervised methods usually employ teacher-student knowledge distillation together with an augmentation strategy to leverage unlabeled point clouds. However, these methods adopt global augmentation with scene-level transformations and hence are sub-optimal for instance-level object detection. In this work, we propose an object-level point augmentor (OPA) that performs local transformations for semi-supervised 3D object detection. In this way, the resultant augmentor is derived to emphasize object instances rather than irrelevant backgrounds, making the augmented data more useful for object detector training. Extensive experiments on the ScanNet and SUN RGB-D datasets show that the proposed OPA performs favorably against the state-of-the-art methods under various experimental settings. The source code will be available at https://github.com/nomiaro/OPA. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()