arXiv:2504.07335v1 Announce Type: new Abstract: We propose DLTPose, a novel method for 6DoF object pose estimation from RGB-D images that combines the accuracy of sparse keypoint methods with the robustness of dense pixel-wise predictions. DLTPose predicts per-pixel radial distances to a set of minimally four keypoints, which are then fed into our novel Direct Linear Transform (DLT) formulation to produce accurate 3D object frame surface estimates, leading to better 6DoF pose estimation. Additionally, we introduce a novel symmetry-aware keypoint ordering approach, designed to handle object symmetries that otherwise cause inconsistencies in keypoint assignments. Previous keypoint-based methods relied on fixed keypoint orderings, which failed to account for the multiple valid configurations exhibited by symmetric objects, which our ordering approach exploits to enhance the model’s ability to learn stable keypoint representations. Extensive experiments on the benchmark LINEMOD, Occlusion LINEMOD and YCB-Video datasets show that DLTPose outperforms existing methods, especially for symmetric and occluded objects, demonstrating superior Mean Average Recall values of 86.5% (LM), 79.7% (LM-O) and 89.5% (YCB-V). The code is available at https://anonymous.4open.science/r/DLTPose_/ .
The article “DLTPose: A Novel Approach for 6DoF Object Pose Estimation from RGB-D Images” introduces a new method that combines the accuracy of sparse keypoint methods with the robustness of dense pixel-wise predictions. The proposed method, DLTPose, predicts per-pixel radial distances to a set of minimally four keypoints, which are then used in a novel Direct Linear Transform (DLT) formulation to produce accurate 3D object frame surface estimates, leading to improved 6DoF pose estimation.
One of the key contributions of DLTPose is a novel symmetry-aware keypoint ordering approach, which addresses the challenges posed by object symmetries that often cause inconsistencies in keypoint assignments. Unlike previous methods that relied on fixed keypoint orderings, DLTPose leverages the multiple valid configurations exhibited by symmetric objects to enhance the model’s ability to learn stable keypoint representations.
The article presents extensive experiments conducted on benchmark datasets, including LINEMOD, Occlusion LINEMOD, and YCB-Video, demonstrating that DLTPose outperforms existing methods, particularly for symmetric and occluded objects. The results show superior Mean Average Recall values of 86.5% (LM), 79.7% (LM-O), and 89.5% (YCB-V) for DLTPose. The code for DLTPose is also made available for further exploration and use.
Unlocking Accurate 6DoF Object Pose Estimation with DLTPose
Advances in computer vision have brought us closer to achieving precise 6DoF (six degrees of freedom) object pose estimation from RGB-D images. However, existing methods often struggle with symmetric and occluded objects, leading to inconsistent and inaccurate results. In this article, we introduce DLTPose, a novel method that combines the accuracy of sparse keypoint methods with the robustness of dense pixel-wise predictions, addressing these challenges and setting a new benchmark for 6DoF pose estimation.
Redefining Keypoint Detection and Pose Estimation
DLTPose leverages the power of per-pixel radial distances to a set of minimally four keypoints. By predicting these distances, we capture detailed information about the object’s shape and structure. These distances are then fed into our Direct Linear Transform (DLT) formulation, which produces accurate 3D object frame surface estimates. This approach improves pose estimation by providing a more comprehensive representation of the object, surpassing the limitations of traditional keypoint methods.
Addressing Object Symmetries with a Novel Keypoint Ordering Approach
A major challenge in accurately estimating the pose of symmetric objects is assigning keypoints in a consistent and stable manner. Previous methods relied on fixed keypoint orderings, overlooking the multiple valid configurations exhibited by symmetric objects. DLTPose tackles this issue by introducing a novel symmetry-aware keypoint ordering approach.
Our ordering approach allows the model to learn stable keypoint representations by exploiting the various valid configurations of the object. By dynamically adapting the ordering of keypoints, DLTPose overcomes inconsistencies caused by object symmetries and significantly enhances the overall performance of the pose estimation model.
Outperforming Existing Methods on Benchmark Datasets
To validate the effectiveness of DLTPose, we conducted extensive experiments on benchmark datasets, including LINEMOD, Occlusion LINEMOD, and YCB-Video. The results unequivocally demonstrate the superiority of DLTPose, especially for symmetric and occluded objects.
DLTPose achieves Mean Average Recall (MAR) values of 86.5% on LINEMOD, 79.7% on Occlusion LINEMOD, and an impressive 89.5% on YCB-Video. These results clearly indicate the remarkable improvement over existing methods, highlighting the potential of DLTPose as a game-changer in the field of 6DoF object pose estimation.
Access the DLTPose Code
The code for DLTPose is openly available at https://anonymous.4open.science/r/DLTPose_. We encourage researchers and practitioners to explore DLTPose and further advance the capabilities of 6DoF object pose estimation.
In conclusion, DLTPose blends the strengths of sparse keypoint methods and dense pixel-wise predictions to deliver unparalleled accuracy in 6DoF object pose estimation. By incorporating a symmetry-aware keypoint ordering approach, DLTPose overcomes limitations posed by symmetric objects, producing consistent and robust results. With its impressive performance on benchmark datasets, DLTPose sets the stage for enhanced applications in robotics, augmented reality, and more. Get hands-on with DLTPose today and unlock the full potential of 6DoF object pose estimation.
The paper titled “DLTPose: A Novel Method for 6DoF Object Pose Estimation from RGB-D Images” introduces a new approach that aims to improve the accuracy and robustness of object pose estimation using a combination of sparse keypoint methods and dense pixel-wise predictions.
The authors propose DLTPose, a method that predicts per-pixel radial distances to a set of minimally four keypoints. These predicted distances are then used in their novel Direct Linear Transform (DLT) formulation to estimate accurate 3D object frame surfaces, which ultimately leads to better 6DoF pose estimation.
One notable contribution of this work is the introduction of a symmetry-aware keypoint ordering approach. This approach addresses the challenge of handling object symmetries that often cause inconsistencies in keypoint assignments. Unlike previous methods that relied on fixed keypoint orderings, which failed to account for multiple valid configurations exhibited by symmetric objects, the proposed ordering approach leverages the knowledge of object symmetries to enhance the model’s ability to learn stable keypoint representations.
To evaluate the performance of DLTPose, extensive experiments were conducted on benchmark datasets including LINEMOD, Occlusion LINEMOD, and YCB-Video. The results showed that DLTPose outperforms existing methods, particularly for symmetric and occluded objects. The Mean Average Recall (MAR) values achieved by DLTPose were 86.5% for LINEMOD, 79.7% for Occlusion LINEMOD, and 89.5% for YCB-Video. These results indicate the superior performance of DLTPose in accurately estimating the 6DoF pose of objects in challenging scenarios.
Overall, DLTPose presents a promising approach for 6DoF object pose estimation from RGB-D images. By combining the strengths of sparse keypoint methods and dense pixel-wise predictions, and incorporating a symmetry-aware keypoint ordering approach, DLTPose demonstrates improved accuracy and robustness compared to existing methods. The availability of the code further enhances the reproducibility and facilitates future research in this area.
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