基于注意力增强的跨模态多层融合网络的物体位姿估计

杨恒,王韶涵,董青,赵科渊,杨明亮

Object pose estimation based on attention enhancement cross-modal multilayer fusion network

Heng YANG,Shaohan WANG,Qing DONG,Keyuan ZHAO,Mingliang YANG

表 2 YCB-Video数据集上ADD(-S)分数和ADD(-S) AUC的定量评估

Tab.2 Quantitative evaluation of ADD(-S) score and ADD(-S) AUC on YCB-Video dataset

方法 ADD(-S)分数/% master- chef- can cracker- box sugar- box tomato- soup- can mustard- bottle tuna- fish- can pudding- box gelatin- box potted- meat- can Banana pitcher- base bleach- cleaner Bowl Mug power- drill wood- block Scissor large- marker large- clamp extra- large- clamp foam- brick 平均值 PointFusion[12] 99.8 62.2 95.4 96.9 84.0 99.8 96.7 100 88.5 70.5 79.8 65.0 24.1 99.8 22.8 18.2 35.9 80.4 50.0 20.1 100 74.1 PVN3D[31] 100 91.6 100 96.9 100 100 100 100 93.6 99.7 100 99.4 54.9 99.8 99.6 80.2 95.6 99.7 74.9 48.8 100 93.2 DenseFusion[13] 100 99.5 100 96.9 100 100 100 100 91.3 100 100 100 98.8 100 98.7 94.6 100 100 79.2 76.3 100 96.8 CFFM+CWTM[23] 100 95.3 100 95.7 100 99.0 93.9 100 90.8 93.4 100 97.8 53.2 98.2 97.4 81.3 94.7 96.7 68.3 62.5 99.6 93.5 FoundationPose[14] 99.1 98.7 99.3 98.9 99.5 99.0 98.8 99.4 98.5 99.2 98.6 98.3 98.0 98.7 98.4 97.5 97.1 98.2 96.9 96.5 99.1 99.0 本文方法 （仅使用多 尺度模块） 98.5 93.1 98.0 89.1 94.3 97.3 93.9 99.9 92.4 95.1 97.4 93.6 92.1 94.1 94.6 93.2 91.0 96.2 88.1 83.2 99.4 94.3 本文方法 （仅使用注意 力增强模块） 99.1 94.9 98.7 90.6 95.7 97.8 94.7 100 93.7 96.3 98.1 95.1 93.5 95.3 95.8 94.7 92.5 97.5 90.3 85.8 99.7 95.2 本文方法 100 97.5 99.0 93.5 97.5 98.5 96.5 100 96.0 97.5 98.0 97.0 99.2 97.0 96.5 98.0 96.5 98.0 85.0 82.0 100 97.1 方法 AUC/% master- chef- can cracker- box sugar- box tomato- soup- can mustard- bottle tuna- fish- can pudding- box gelatin- box potted- meat- can Banana pitcher- base bleach- cleaner Bowl Mug power- drill wood- block Scissor large- marker large- clamp extra- large- clamp foam- brick 平均值 PointFusion[12] 90.9 80.5 90.4 91.9 88.5 93.8 87.5 95.0 86.4 84.7 85.5 81.0 75.7 94.2 71.5 68.1 76.7 87.9 65.9 60.4 91.8 83.9 PVN3D[31] 95.8 92.7 98.2 94.5 98.6 97.1 97.9 98.8 92.7 97.1 97.8 96.9 81.0 95.0 98.2 87.6 91.7 97.2 75.2 64.4 97.2 93.0 DenseFusion[13] 96.4 95.5 97.5 94.6 97.2 96.6 96.5 98.1 91.3 96.6 97.1 95.8 88.2 97.1 96.0 89.7 95.2 97.5 72.9 69.8 92.5 93.1 CFFM+CWTM[23] 94.3 92.2 98.7 93.9 97.1 94.8 93.3 99.1 91.6 91.1 95.2 86.5 83.8 90.5 94.8 84.3 92.8 88.6 73.2 62.3 93.3 90.5 FoundationPose[14] 96.5 94.2 96.8 95.0 97.4 96.1 94.5 97.0 93.8 96.3 94.0 92.5 91.8 94.3 93.1 89.5 88.2 92.0 87.5 85.4 95.9 93.2 本文方法 （仅使用多 尺度模块） 93.9 84.3 93.7 80.9 90.8 91.3 89.2 97.8 85.9 87.9 93.6 89.4 79.5 89.1 87.2 85.4 78.7 89.3 73.5 68.7 94.0 88.5 本文方法 （仅使用注意 力增强模块） 94.7 86.7 94.4 82.7 92.0 92.7 90.3 98.3 88.2 89.7 94.8 91.0 82.7 90.6 88.9 87.7 82.3 91.0 75.2 71.5 95.5 89.7 本文方法 96.9 98.6 98.6 95.0 96.5 94.5 93.1 99.0 90.3 91.8 95.5 93.4 88.3 92.5 90.5 100 85.5 98.4 82.5 80.5 97.0 93.7