2 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.CV2023
Context-Aware Transformer for 3D Point Cloud Automatic Annotation
Xiaoyan Qian, Chang Liu, Xiaojuan Qi +3
3D automatic annotation has received increased attention since manually annotating 3D point clouds is laborious. However, existing methods are usually complicated, e.g., pipelined…
cs.CV2022★ 2 cited
Multimodal Transformer for Automatic 3D Annotation and Object Detection
Chang Liu, Xiaoyan Qian, Binxiao Huang +4
Despite a growing number of datasets being collected for training 3D object detection models, significant human effort is still required to annotate 3D boxes on LiDAR scans. To aut…
cs.CV2022★ 2 cited
MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator
Chang Liu, Xiaoyan Qian, Xiaojuan Qi +3
Manually annotating 3D point clouds is laborious and costly, limiting the training data preparation for deep learning in real-world object detection. While a few previous studies t…