用关键点热图回归实现6D姿态估计，结合深度信息提升精度
6D Pose Estimation via Keypoint Heatmap Regression with RGB-D Residual Neural Networks
- 基于YOLOv10m与残差网络，通过热图回归提取关键点
- RGB-D融合模型在LINEMOD上达到92.41%的平均ADD准确率
- 适合需要高精度姿态估计的机器人抓取与视觉定位场景
本文提出一种基于关键点热图回归的6D姿态估计模块化框架。采用YOLOv10m进行物体检测，并使用基于ResNet18的网络从彩色图像预测2D热图。从热图中提取的关键点通过PnP RANSAC算法估计6D物体姿态。对比了不同关键点选择策略对姿态精度的影响。进一步引入深度数据，设计跨模态融合架构，在多阶段实现RGB与深度特征交互。还探索了激活函数和学习率调度策略以优化训练。最佳仅含RGB的模型在LINEMOD数据集上达到84.50%的平均ADD准确率，而RGB-D融合模型达92.41%。代码已开源。
原文摘要 · Abstract (English)
In this paper, we propose a modular framework for 6D pose estimation based on keypoint heatmap regression. Our approach combines YOLOv10m for object detection with a ResNet18-based network that predicts 2D heatmaps from RGB images. Keypoints extracted from these heatmaps are used to estimate the 6D object pose via the PnP RANSAC algorithm. We compare different keypoint selection strategies to assess their impact on pose accuracy. Additionally, we extend the baseline by incorporating depth data using a cross-fusion architecture, which enables interaction between RGB and depth features at multiple stages. We further explore general training improvements, such as experimenting with activation functions and learning rate scheduling strategies to improve model performance. Our best RGB-only model achieved a mean ADD-based accuracy of 84.50%, while the RGB-D fusion model reached 92.41% on the LINEMOD dataset. The code is available at https://github.com/ameermasood/HeatNet.
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