[1] A TKACH, A TAGLIASACCHI, E REMELLI et al. Online generative model personalization for hand tracking. ACM Transactions on Graphics, 36, 243(2017).

[2] [2] 2李东年， 周以齐. 采用改进粒子群优化粒子滤波的三维人手跟踪［J］. 光学 精密工程， 2014， 22（10）： 2870-2878. doi: 10.3788/ope.20142210.2870LID N， ZHOUY Q. Three dimensional hand tracking by improved particle swarm optimized particle filter［J］. Opt. Precision Eng.， 2014， 22（10）： 2870-2878.（in Chinese）. doi: 10.3788/ope.20142210.2870

[3] X TANG, T Y WANG, C W FU. Towards accurate alignment in real-time 3D hand-mesh reconstruction, 11678-11687(2021).

[4] F XIONG, B S ZHANG, Y XIAO et al. A2J： anchor-to-joint regression network for 3D articulated pose estimation from a single depth image, 793-802(2019).

[5] J TAYLOR, V TANKOVICH, D H TANG et al. Articulated distance fields for ultra-fast tracking of hands interacting. ACM Transactions on Graphics, 36, 244(2017).

[6] P KREJOV, A GILBERT, R BOWDEN. Guided optimisation through classification and regression for hand pose estimation. Computer Vision and Image Understanding, 155, 124-138(2017).

[7] X H CHEN, G J WANG, H K GUO et al. Pose guided structured region ensemble network for cascaded hand pose estimation. Neurocomputing, 395, 138-149(2020).

[8] W C CHENG, J H PARK, J H KO. HandFoldingNet： a 3D hand pose estimation network using multiscale-feature guided folding of a 2D hand skeleton, 11240-11249(2021).

[9] P F REN, H F SUN, W T HUANG et al. Spatial-aware stacked regression network for real-time 3D hand pose estimation. Neurocomputing, 437, 42-57(2021).

[10] [10] 10李伟强， 雷航， 张静玉， 等. 基于标签分布学习的三维手部姿态估计［J］. 计算机应用， 2021， 41（2）： 550-555.LIW Q， LEIH， ZHANGJ Y， et al. 3D hand pose estimation based on label distribution learning［J］. Journal of Computer Applications， 2021， 41（2）： 550-555.（in Chinese）

[11] W T HUANG, P F REN, J Y WANG et al. AWR： adaptive weighting regression for 3D hand pose estimation. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11061-11068(2020).

[12] [12] 12危德健， 王文明， 王全玉， 等. 改进的基于锚点的三维手部姿态估计网络［J］. 计算机应用， 2022， 42（3）： 953-959.WEID J， WANGW M， WANGQ Y， et al. Improved 3D hand pose estimation network based on anchor［J］. Journal of Computer Applications， 2022， 42（3）： 953-959.（in Chinese）

[13] G MOON, K M LEE. I2L-MeshNet： image-to-lixel prediction network for accurate 3D human pose and mesh estimation from a single RGB image, 752-768(2020).

[14] [14] 14熊杰， 彭军， 杨文姬， 等. 多尺度高分辨率保持和视角不变的手姿态估计［J］. 计算机工程与应用， 2021， 57（14）： 148-157. doi: 10.3778/j.issn.1002-8331.2004-0078XIONGJ， PENGJ， YANGW J， et al. Multi-scale high-resolution preserving and perspective-invariant hand pose estimation［J］. Computer Engineering and Applications， 2021， 57（14）： 148-157.（in Chinese）. doi: 10.3778/j.issn.1002-8331.2004-0078

[15] M R LI, Y GAO, N SANG. Exploiting learnable joint groups for hand pose estimation. 2020： arXiv, 2012-09496. https：//arxiv.org/abs/2012.09496

[16] T SHARP, C KESKIN, D ROBERTSON et al. Accurate， robust， and flexible real-time hand tracking, 3633-3642(2015).

[17] H LIANG, J S YUAN, J LEE et al. Hough forest with optimized leaves for global hand pose estimation with arbitrary postures. IEEE Transactions on Cybernetics, 49, 527-541(2019).

[18] N RUIZ, E CHONG, J M REHG. Fine-grained head pose estimation without keypoints, 2155-215509(2018).

[19] T Y YANG, Y T CHEN, Y LIN et al. FSA-net： learning fine-grained structure aggregation for head pose estimation from a single image, 1087-1096(2019).

[20] [20] 20车云龙， 齐越. 基于深度图像的手部姿态估计综述［J］. 计算机辅助设计与图形学学报， 2021， 33（11）： 1635-1648. doi: 10.3724/sp.j.1089.2021.18788CHEY L， QIY. A survey on depth based hand pose estimation［J］. Journal of Computer-Aided Design ＆ Computer Graphics， 2021， 33（11）： 1635-1648.（in Chinese）. doi: 10.3724/sp.j.1089.2021.18788

[21] L C CHEN, Y K ZHU, G PAPANDREOU et al. Encoder-decoder with atrous separable convolution for semantic image segmentation, 801-818(2018).

[22] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(2016).

[23] [23] 23徐胜军， 欧阳朴衍， 郭学源， 等. 多尺度特征融合空洞卷积 ResNet遥感图像建筑物分割［J］. 光学 精密工程， 2020， 28（7）： 1588-1599. doi: 10.37188/ope.20202807.1588XUSH J， OUYANGP Y， GUOX Y， et al. Building segmentation in remote sensing image based on multiscale-feature fusion dilated convolution resnet［J］. Opt. Precision Eng.， 2020， 28（7）： 1588-1599.（in Chinese）. doi: 10.37188/ope.20202807.1588

[24] O RUSSAKOVSKY, J DENG, H SU et al. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115, 211-252(2015).

[25] [25] 25刘桂雄， 黄坚. 基于标签预留Softmax算法的机器视觉检测鉴别语义分割迁移学习技术［J］. 光学 精密工程， 2022， 30（1）： 117-125. doi: 10.37188/OPE.20223001.0117LIUG X， HUANGJ. Transfer learning techniques for semantic segmentation of machine vision inspection and identification based on label-reserved Softmax algorithms［J］. Optics and Precision Engineering， 2022， 30（1）： 117-125.（in Chinese）. doi: 10.37188/OPE.20223001.0117

[26] Y ZHOU, C BARNES, J W LU et al. On the continuity of rotation representations in neural networks, 5738-5746(2019).

[27] Y XIANG, T SCHMIDT, V NARAYANAN et al. Posecnn： A Convolutional Neural Network for 6D Object Pose Estimation In Cluttered Scenes. arXiv preprint(2017).