Rgb-D Fusion For Point-Cloud-Based 3d Human Pose Estimation
Jiaming Ying, Xu Zhao
Abstract
3D human pose estimation is an important and challenging task in computer vision. In this paper, we propose a method to estimate 3D human pose from RGB-D images. We adopt a 2D pose estimator to extract color features from the RGB image. The color features are integrated with the depth image in the form of point cloud. To fully exploit geometric information, we design a 3D learning module to extract point-wise features. To take advantage of local information as well as facilitate the convergence of the model, we design a dense prediction module. It estimates the offset vectors and closeness scores from points to target keypoints. The point-wise estimations are weighted and summed up to a final 3D pose. Experimental results show that our method achieves state-of-the-art performance on MHAD and SURREAL datasets.