W-PoseNet：稠密对应正则化的像素对姿态回归
W-PoseNet: Dense Correspondence Regularized Pixel Pair Pose Regression
AI总结：
针对6D姿态估计受遮挡、光照等影响的难题，提出W-PoseNet算法，通过稠密对应正则化、像素对特征组合与软投票提升鲁棒性，在YCB-Video、LineMOD基准上性能优于现有最优方法。
摘要：
6D姿态估计面临固有外观与形状变化、严重物体间遮挡的难题，非受控环境下的外部光照大幅变化、采集数据质量低更使其颇具挑战。本文提出一种新型姿态估计算法W-PoseNet，可从输入数据稠密回归6D姿态以及模型空间中的3D坐标。换言之，我们的深度网络中为姿态回归学习的局部特征，通过显式学习映射到3D姿态敏感坐标的逐像素对应作为辅助任务来实现正则化。此外，我们设计了逐像素特征的稀疏对组合以及像素对姿态预测的软投票机制，以提升对不一致、稀疏局部特征的鲁棒性。在主流基准YCB-Video和LineMOD上的实验结果表明，所提出的W-PoseNet性能始终优于现有最优算法。
英文摘要：
Solving 6D pose estimation is non-trivial to cope with intrinsic appearance and shape variation and severe inter-object occlusion, and is made more challenging in light of extrinsic large illumination changes and low quality of the acquired data under an uncontrolled environment. This paper introduces a novel pose estimation algorithm W-PoseNet, which densely regresses from input data to 6D pose and also 3D coordinates in model space. In other words, local features learned for pose regression in our deep network are regularized by explicitly learning pixel-wise correspondence mapping onto 3D pose-sensitive coordinates as an auxiliary task. Moreover, a sparse pair combination of pixel-wise features and soft voting on pixel-pair pose predictions are designed to improve robustness to inconsistent and sparse local features. Experiment results on the popular YCB-Video and LineMOD benchmarks show that the proposed W-PoseNet consistently achieves superior performance to the state-of-the-art algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
