SESS: Self-Ensembling Semi-Supervised 3D Object Detection
This addresses the data annotation bottleneck for 3D object detection in computer vision, offering a semi-supervised alternative to reduce labeling costs.
The paper tackles the problem of expensive 3D annotations for point cloud-based object detection by proposing SESS, a self-ensembling semi-supervised framework, achieving competitive performance with state-of-the-art fully-supervised methods using only 50% labeled data on SUN RGB-D and ScanNet datasets.
The performance of existing point cloud-based 3D object detection methods heavily relies on large-scale high-quality 3D annotations. However, such annotations are often tedious and expensive to collect. Semi-supervised learning is a good alternative to mitigate the data annotation issue, but has remained largely unexplored in 3D object detection. Inspired by the recent success of self-ensembling technique in semi-supervised image classification task, we propose SESS, a self-ensembling semi-supervised 3D object detection framework. Specifically, we design a thorough perturbation scheme to enhance generalization of the network on unlabeled and new unseen data. Furthermore, we propose three consistency losses to enforce the consistency between two sets of predicted 3D object proposals, to facilitate the learning of structure and semantic invariances of objects. Extensive experiments conducted on SUN RGB-D and ScanNet datasets demonstrate the effectiveness of SESS in both inductive and transductive semi-supervised 3D object detection. Our SESS achieves competitive performance compared to the state-of-the-art fully-supervised method by using only 50% labeled data. Our code is available at https://github.com/Na-Z/sess.