DLTPose: 6DoF Pose Estimation From Accurate Dense Surface Point Estimates
This addresses the problem of accurate and robust pose estimation for symmetric and occluded objects in computer vision applications, representing a strong specific gain rather than a foundational advancement.
The paper tackles 6DoF object pose estimation from RGBD images by proposing DLTPose, which combines sparse keypoint accuracy with dense pixel-wise robustness and introduces a symmetry-aware keypoint ordering approach, resulting in outperforming existing methods on benchmark datasets like LINEMOD, Occlusion LINEMOD, and YCB-Video, especially for symmetric and occluded objects.
We propose DLTPose, a novel method for 6DoF object pose estimation from RGBD 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. The code is available at https://anonymous.4open.science/r/DLTPose_/ .