解决遮挡下多物体6D姿态估计难题，通过掩码感知与非可视区域重建提升精度。
MAPRPose: Mask-Aware Proposal and Amodal Refinement for Multi-Object 6D Pose Estimation
- 两阶段框架：先生成姿态候选，再用非可视掩码动态优化感兴趣区域。
- 在LINEMOD和YCB-W Challenge数据集上，平均精度达83.4%和79.1%。
- 适合做高精度机器人抓取、自动驾驶中复杂场景的物体定位任务。
在杂乱场景中进行6D物体姿态估计仍面临严重遮挡和传感器噪声的挑战。我们提出MAPRPose，一种两阶段框架，利用掩码感知对应关系生成姿态候选，并通过非可视驱动的感兴趣区域（ROI）预测实现鲁棒精炼。在掩码感知姿态提案（MAPP）阶段，将2D对应点提升至3D空间以建立可靠的特征匹配，并基于对应关系评分生成几何一致的姿态假设，从中选出前K个最优候选。在精炼阶段，引入张量化渲染-比较流水线，结合非可视掩码预测与ROI重对齐（AMPR）模块。通过重构完整物体几何结构并动态调整ROI，AMPR有效缓解了重度遮挡下的定位误差与空间错位问题。此外，我们的GPU加速的RGB-XYZ重投影技术可在一次前向传播中同时精炼N×B个姿态假设。
原文摘要 · Abstract (English)
6D object pose estimation in cluttered scenes remains challenging due to severe occlusion and sensor noise. We propose MAPRPose, a two-stage framework that leverages mask-aware correspondences for pose proposal and amodal-driven Region-of-Interest (ROI) prediction for robust refinement. In the Mask-Aware Pose Proposal (MAPP) stage, we lift 2D correspondences into 3D space to establish reliable keypoint matches and generate geometrically consistent pose hypotheses based on correspondence-level scoring, from which the top-$K$ candidates are selected. In the refinement stage, we introduce a tensorized render-and-compare pipeline integrated with an Amodal Mask Prediction and ROI Re-Alignment (AMPR) module. By reconstructing complete object geometry and dynamically adjusting the ROI, AMPR mitigates localization errors and spatial misalignment under heavy occlusion. Furthermore, our GPU-accelerated RGB-XYZ reprojection enables simultaneous refinement of all $N \times B$ pose hypotheses in a single forward pass.
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