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Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation

机译:Pix2Pose:用于6D姿势估计的对象的像素明智坐标回归

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Estimating the 6D pose of objects using only RGB images remains challenging because of problems such as occlusion and symmetries. It is also difficult to construct 3D models with precise texture without expert knowledge or specialized scanning devices. To address these problems, we propose a novel pose estimation method, Pix2Pose, that predicts the 3D coordinates of each object pixel without textured models. An auto-encoder architecture is designed to estimate the 3D coordinates and expected errors per pixel. These pixel-wise predictions are then used in multiple stages to form 2D-3D correspondences to directly compute poses with the PnP algorithm with RANSAC iterations. Our method is robust to occlusion by leveraging recent achievements in generative adversarial training to precisely recover occluded parts. Furthermore, a novel loss function, the transformer loss, is proposed to handle symmetric objects by guiding predictions to the closest symmetric pose. Evaluations on three different benchmark datasets containing symmetric and occluded objects show our method outperforms the state of the art using only RGB images.
机译:由于诸如遮挡和对称性之类的问题,仅使用RGB图像估计对象的6D姿势仍然具有挑战性。没有专家知识或专门的扫描设备,也很难构建具有精确纹理的3D模型。为了解决这些问题,我们提出了一种新颖的姿态估计方法Pix2Pose,它可以在没有纹理模型的情况下预测每个对象像素的3D坐标。自动编码器体系结构旨在估计3D坐标和每个像素的预期误差。然后将这些按像素进行的预测在多个阶段中使用以形成2D-3D对应关系,以直接通过带有RANSAC迭代的PnP算法计算姿态。我们的方法通过利用对抗性生成训练中的最新成果来精确地恢复被遮挡的部分,从而对遮挡具有鲁棒性。此外,提出了一种新颖的损耗函数,即变压器损耗,通过将预测引导至最接近的对称姿态来处理对称对象。对包含对称对象和遮挡对象的三个不同基准数据集的评估表明,仅使用RGB图像,我们的方法就优于现有技术。

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