TY - GEN
T1 - Point-to-point regression pointnet for 3D hand pose estimation
AU - Ge, Liuhao
AU - Ren, Zhou
AU - Yuan, Junsong
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Convolutional Neural Networks (CNNs)-based methods for 3D hand pose estimation with depth cameras usually take 2D depth images as input and directly regress holistic 3D hand pose. Different from these methods, our proposed Point-to-Point Regression PointNet directly takes the 3D point cloud as input and outputs point-wise estimations, i.e., heat-maps and unit vector fields on the point cloud, representing the closeness and direction from every point in the point cloud to the hand joint. The point-wise estimations are used to infer 3D joint locations with weighted fusion. To better capture 3D spatial information in the point cloud, we apply a stacked network architecture for PointNet with intermediate supervision, which is trained end-to-end. Experiments show that our method can achieve outstanding results when compared with state-of-the-art methods on three challenging hand pose datasets.
AB - Convolutional Neural Networks (CNNs)-based methods for 3D hand pose estimation with depth cameras usually take 2D depth images as input and directly regress holistic 3D hand pose. Different from these methods, our proposed Point-to-Point Regression PointNet directly takes the 3D point cloud as input and outputs point-wise estimations, i.e., heat-maps and unit vector fields on the point cloud, representing the closeness and direction from every point in the point cloud to the hand joint. The point-wise estimations are used to infer 3D joint locations with weighted fusion. To better capture 3D spatial information in the point cloud, we apply a stacked network architecture for PointNet with intermediate supervision, which is trained end-to-end. Experiments show that our method can achieve outstanding results when compared with state-of-the-art methods on three challenging hand pose datasets.
KW - 3D hand pose estimation
UR - https://www.scopus.com/pages/publications/85055117273
U2 - 10.1007/978-3-030-01261-8_29
DO - 10.1007/978-3-030-01261-8_29
M3 - Conference contribution
AN - SCOPUS:85055117273
SN - 9783030012601
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 489
EP - 505
BT - Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
A2 - Ferrari, Vittorio
A2 - Sminchisescu, Cristian
A2 - Weiss, Yair
A2 - Hebert, Martial
PB - Springer Verlag
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -