Context-Aware Transformer for 3D Point Cloud Automatic Annotation
This addresses the need for efficient 3D annotation in autonomous driving and robotics, but it is incremental as it builds on existing transformer and annotation methods.
The paper tackles the problem of laborious manual annotation of 3D point clouds by proposing a Context-Aware Transformer (CAT) to generate precise 3D box annotations from 2D boxes, achieving a 1.79% improvement in 3D AP on the hard task of the KITTI test set.
3D automatic annotation has received increased attention since manually annotating 3D point clouds is laborious. However, existing methods are usually complicated, e.g., pipelined training for 3D foreground/background segmentation, cylindrical object proposals, and point completion. Furthermore, they often overlook the inter-object feature relation that is particularly informative to hard samples for 3D annotation. To this end, we propose a simple yet effective end-to-end Context-Aware Transformer (CAT) as an automated 3D-box labeler to generate precise 3D box annotations from 2D boxes, trained with a small number of human annotations. We adopt the general encoder-decoder architecture, where the CAT encoder consists of an intra-object encoder (local) and an inter-object encoder (global), performing self-attention along the sequence and batch dimensions, respectively. The former models intra-object interactions among points, and the latter extracts feature relations among different objects, thus boosting scene-level understanding. Via local and global encoders, CAT can generate high-quality 3D box annotations with a streamlined workflow, allowing it to outperform existing state-of-the-art by up to 1.79% 3D AP on the hard task of the KITTI test set.