基于循环神经网络的双目视觉物体6D位姿估计
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杨恒,李卓,康忠元,田兵,董青
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Binocular vision object 6D pose estimation based on circulatory neural network
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Heng YANG,Zhuo LI,Zhong-yuan KANG,Bing TIAN,Qing DONG
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| 表 1 Binocular-RNN与其他方法在YCB-Video Dataset上的比较 |
| Tab.1 Comparison of Binocular-RNN with other methods on YCB-Video Dataset |
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| 方法 | Ref | m | Acc(ADD(S))/
% | AUC/% | ts/ms | | ADDS | ADD(S) | | Only-RNN | — | 1 | — | — | — | — | | Only-CNN | — | 1 | 18.4 | 62.3 | 59.6 | 35 | | Only-CNN | — | M | 15.6 | — | — | — | | Binocular-RNN | — | 1 | 56.7 | 90.8 | 85.2 | 23 | | Binocular-RNN | — | M | 70.5 | 93.4 | 89.6 | — | | PoseCNN[19] | — | 1 | 21.3 | 75.9 | 61.3 | 24 | | GDR-Net | — | 1 | 49.1 | 89.1 | 80.2 | 22 | | GDR-Net | — | M | 60.1 | 91.6 | 84.4 | — | | Single-Stage[16] | — | M | 53.9 | — | — | — | | DeepIM[19] | √ | 1 | — | 88.1 | 81.9 | 25 | | CosyPose[20] | √ | 1 | — | 89.8 | 84.5 | 25 |
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