农业机械学报2026，Vol.57Issue(5)：149-158,10.DOI:10.6041/j.issn.1000-1298.2026.05.013
基于改进RTMPose3D模型的番茄三维关键点估计方法
Tomato 3D Keypoint Estimation Method Based on Improved RTMPose3D Model
摘要
Abstract
Aiming to address the challenge of reliably acquiring 3D pose information of truss tomatoes for autonomous harvesting robots under conditions of severe occlusion and strong light interference in greenhouses,an improved 3D keypoint estimation model named TomatoPose3D was proposed.During the training phase,the model incorporated joint constraints between RGB images and 3D ground-truth keypoints to enhance structural consistency and generalization capability.In the inference phase,the model can end-to-end regress 3D keypoint coordinates from a single RGB image,thereby avoiding localization failures caused by sparse or missing point clouds.Based on the RTMPose3D baseline,the improved model introduced the global structure-aware MobileVit Block and the distribution-aware coordinate representation of keypoints(DARK)decoding strategy,improving localization accuracy while maintaining a lightweight architecture.Comparative experiments in greenhouse scenarios indicated that TomatoPose3D improved the PCK@0.05 score by 5.18 and 9.98 percentage points compared with RTMPose3D and SimpleBaseline3D,respectively.Without the assistance of depth information,the model achieved localization accuracy comparable to RGB-D projection-based methods while demonstrating superior robustness.Furthermore,the model was deployed on an industrial-grade embedded platform accelerated by TensorRT,achieving an end-to-end inference speed of 37 f/s,which met the real-time spatial visual perception requirements of harvesting robots.关键词
温室番茄/采摘机器人/三维关键点估计/轻量化网络/RTMPose3DKey words
greenhouse tomato/harvesting robot/3D keypoint estimation/lightweight network/RTMPose3D分类
农业科技引用本文复制引用
王蓬勃,刘宇,赵胜辉,傅毅凯..基于改进RTMPose3D模型的番茄三维关键点估计方法[J].农业机械学报,2026,57(5):149-158,10.基金项目
苏州市科技强农创新项目(SNG2025009)和国家重点研发计划项目(2022YFB4702202) （SNG2025009）
