- The paper introduces a modular framework that integrates keypoint heatmap regression with a dual‐stream RGB-D cross-fusion network.
- The paper demonstrates that FPS keypoint sampling combined with robust detection yields improved pose accuracy, achieving up to 92.41% on LINEMOD.
- The paper validates its approach through experiments that highlight trade-offs in activation functions and learning rate scheduling for optimal performance.
Modular Keypoint Heatmap Regression for 6D Pose Estimation Using RGB-D Residual Networks
Methodological Approach
The paper details a modular framework for 6D pose estimation utilizing keypoint heatmap regression, integrating both RGB and depth modalities via a cross-fusion neural architecture. The pipeline commences with object detection using YOLOv10m, followed by region cropping and resizing to standardize input for downstream stages. For pose estimation, two strategies for keypoint selection, Farthest Point Sampling (FPS) and Curvature Point Sampling (CPS), are investigated, with keypoints extracted from CAD models projected to image space.
A ResNet18-based feature extractor is employed to regress 2D heatmaps from cropped RGB images, providing a probabilistic spatial representation for each keypoint. Pose estimation is effectuated through correspondences between predicted 2D keypoints and sampled 3D points, resolved by a PnP algorithm with RANSAC for robust outlier rejection.
Depth information is incorporated through a dual-stream cross-fusion ResNet18 architecture, enabling bidirectional feature exchange at multiple levels. The cross-fusion module leverages residual blocks to integrate geometry from depth and visual cues from RGB, resulting in more discriminative fused representations for keypoint localization.
Dataset and Experimental Setup
A subset of the LINEMOD dataset is used, focusing on 13 object classes with a total of 15,800 RGB images and corresponding depth maps. The training pipeline exploits both keypoint heatmap regression and classical object detection, ensuring the downstream model is exposed to detection-induced localization errors, which bolsters robustness.
The training framework adopts the Adam optimizer, focal heatmap loss to counterbalance class imbalance, and experiments with several activation functions (ReLU, SiLU, Mish) and learning rate schedulers (OneCycleLR, PolynomialLR) to optimize convergence and generalization.
Results and Numerical Analysis
On the LINEMOD benchmark, the baseline RGB-only model achieves a mean ADD-based pose accuracy of 84.50%. The RGB-D fusion model reaches 92.41%. FPS outperforms CPS, particularly on challenging objects, including those with symmetry or low texture (e.g., glue, eggbox, ape, duck). The YOLOv10m detector produces consistently high mAP (mean = 0.974), indicating negligible error propagation from detection to pose estimation.
Augmenting the model with depth data via cross-fusion architecture improves average accuracy to 88.19% in validation, with notable per-class gains on difficult examples. Activation function experiments reveal Mish produces marginal gains on symmetric objects, but SiLU degrades performance unless paired with appropriate learning rate scheduling. The combination of Mish activation and OneCycleLR scheduling yields maximal improvement, corroborating the importance of dynamic learning rate modulation.
Best numerical results are achieved using a cross-fusion RGB-D model with ReLU activation and OneCycleLR scheduling; mean test accuracy of 92.41% is reported. These results are robust to object class and demonstrate strong generalization.
Comparative Evaluation and Practical Implications
The approach synthesizes established concepts in the literature: feature-based keypoint detection, heatmap regression, and geometric pose solvers. Compared to prior art employing pixelwise voting or coordinate regression, the heatmap method delivers enhanced spatial gradients for stable training and converges rapidly.
FPS is confirmed as the most effective sampling strategy for keypoint distribution, especially in scenarios involving symmetry, texture-less surfaces, or occlusion, thereby justifying its widespread adoption.
The integration of depth via a sophisticated cross-fusion module rather than naïve early fusion yields better spatial precision, but is dependent on depth sensor quality and alignment. Practical deployment is thus contingent on the fidelity of depth data and independent calibration.
The system is modular, which facilitates extensibility to additional modalities or network variants. Its architecture is computationally efficient and suitable for real-time inference, aligning with robotics, AR, and autonomous navigation requirements.
Theoretical Implications and Future Directions
The results substantiate the proposition that heatmap regression paired with geometric solvers is a performant paradigm for 6D pose estimation. Cross-modality fusion improves robustness in cluttered, occluded, and ambiguous environments but leaves residual challenges for certain classes. Further improvements might be achieved by deeper architectures, advanced augmentation, adaptive keypoint count selection, or end-to-end optimization strategies coupling detection and pose stages.
Model performance is sensitive to training configuration; future research could formalize the dependence of convergence and accuracy on activation and learning rate scheduling, potentially automating hyperparameter search.
Additionally, advanced fusion techniques and transformer-based architectures could be explored to further enhance multi-modal feature integration. Expansion to domain adaptation, scene-level pose estimation, or joint multi-object fusion is a prospective research avenue.
Conclusion
This paper presents a comprehensive framework for 6D pose estimation from RGB-D input, combining keypoint heatmap regression, dual-stream cross-fusion networks, and robust geometric pose solving. Empirical evaluation demonstrates high accuracy and strong robustness across the LINEMOD benchmark, particularly when leveraging depth and optimized training configurations. The proposed modular pipeline offers a scalable foundation for further innovations in object pose estimation, with direct implications for robotics, AR, and industrial vision systems (2605.08059).
