Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection

Cheng Ju Ho, Chen Hsuan Tai, Yi Hsuan Tsai, Yen Yu Lin, Ming Hsuan Yang

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

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.

Original languageEnglish
StatePublished - 2022
Event33rd British Machine Vision Conference Proceedings, BMVC 2022 - London, United Kingdom
Duration: 21 Nov 202224 Nov 2022

Conference

Conference33rd British Machine Vision Conference Proceedings, BMVC 2022
Country/TerritoryUnited Kingdom
CityLondon
Period21/11/2224/11/22

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