LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network

arXiv:2306.12525v2 Announce Type: replace
Abstract: Due to the difficulty of acquiring large-scale 3D human keypoint annotation, previous methods for 3D human pose estimation (HPE) have often relied on 2D image features and sequential 2D annotations. Furthermore, the training of these networks typically assumes the prediction of a human bounding box and the accurate alignment of 3D point clouds with 2D images, making direct application in real-world scenarios challenging. In this paper, we present the 1st framework for end-to-end 3D human …

2d image abstract annotation annotations arxiv box cs.cv features hpe human image lidar network networks prediction scale training transformer type