Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D Human Pose Estimation

TL;DR

This paper introduces a density-aware pose transformer for LiDAR-based 3D human pose estimation, leveraging intrinsic point cloud properties and augmentation techniques to improve robustness and achieve state-of-the-art results.

Contribution

It proposes a novel density-aware pose transformer and a comprehensive augmentation method for pre-training, enhancing robustness in low-quality LiDAR point clouds.

Findings

Achieves state-of-the-art performance on multiple datasets.

Reduces MPJPE by 10.0mm on Waymo dataset.

Reduces MPJPE by 20.7mm on SLOPER4D dataset.

Abstract

With the rapid development of autonomous driving, LiDAR-based 3D Human Pose Estimation (3D HPE) is becoming a research focus. However, due to the noise and sparsity of LiDAR-captured point clouds, robust human pose estimation remains challenging. Most of the existing methods use temporal information, multi-modal fusion, or SMPL optimization to correct biased results. In this work, we try to obtain sufficient information for 3D HPE only by modeling the intrinsic properties of low-quality point clouds. Hence, a simple yet powerful method is proposed, which provides insights both on modeling and augmentation of point clouds. Specifically, we first propose a concise and effective density-aware pose transformer (DAPT) to get stable keypoint representations. By using a set of joint anchors and a carefully designed exchange module, valid information is extracted from point clouds with different densities. Then 1D heatmaps are utilized to represent the precise locations of the keypoints. Secondly, a comprehensive LiDAR human synthesis and augmentation method is proposed to pre-train the model, enabling it to acquire a better human body prior. We increase the diversity of point clouds by randomly sampling human positions and orientations and by simulating occlusions through the addition of laser-level masks. Extensive experiments have been conducted on multiple datasets, including IMU-annotated LidarHuman26M, SLOPER4D, and manually annotated Waymo Open Dataset v2.0 (Waymo), HumanM3. Our method demonstrates SOTA performance in all scenarios. In particular, compared with LPFormer on Waymo, we reduce the average MPJPE by $10.0mm$. Compared with PRN on SLOPER4D, we notably reduce the average MPJPE by $20.7mm$.