Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection
This work addresses the challenge of reducing annotation costs for 3D object detection in point clouds, which is important for applications like autonomous driving and robotics, but it is incremental as it builds on existing teacher-student frameworks with a novel augmentation strategy.
The paper tackles the problem of semi-supervised 3D object detection by proposing an object-level point augmentor (OPA) that performs local transformations to emphasize object instances, achieving favorable performance against state-of-the-art methods on ScanNet and SUN RGB-D datasets.
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.