EdgePose: An Edge Attention Network for 6D Pose Estimation
Abstract
:1. Introduction
2. Related Works
2.1. RGB-Based Methods
2.1.1. Template-Based Methods
2.1.2. Correspondence-Based Methods
2.1.3. Voting-Based Methods
2.2. RGBD-Based Methods
2.2.1. Template-Based Methods
2.2.2. Correspondence-Based Methods
2.2.3. Voting-Based Methods
2.3. Datasets
3. Proposed Approach
3.1. Edge Attention
3.2. Feature Extraction Network
3.3. Dataset
3.4. Loss Function
3.5. Evaluation Matrics
3.6. Results
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Categories | Correspondence-Based | Template-Based | Voting-Based |
---|---|---|---|
RGB-based | [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19] | [20,21,22,23,24,25] | [26,27] |
RGBD-based | [28,29,30,31,32,33,34,35,36] | [37,38] | [39,40,41,42,43] |
Dataset | Modality | |||
---|---|---|---|---|
LineMOD [49] | RGBD | - | 13 | 18,273 |
YCB-V [50] | RGBD | - | 21 | 133,936 |
T-less [51] | RGBD | - | 30 | 47,664 |
NOCS [5] | RGBD | 6 | 1085 | 300,000 |
ShapeNet6D [33] | RGBD | 51 | 12,490 | 800,000 |
Objects | PoseCNN [7] | PVN3D [43] | Uni6d [34] | DeepIM [56] | FS6D [33] | FFB6D [29] | Ours |
---|---|---|---|---|---|---|---|
master chef can | 50.9 | 80.5 | 70.2 | 71.2 | 36.8 | 80.6 | 80.7 |
cracker box | 51.7 | 94.8 | 85.2 | 83.6 | 24.5 | 94.6 | 95.3 |
sugar box | 68.6 | 96.3 | 94.5 | 94.1 | 43.9 | 96.6 | 97.9 |
tomato soup can | 66.0 | 88.5 | 85.4 | 86.1 | 54.2 | 89.6 | 88.7 |
mustard bottle | 79.9 | 96.2 | 91.7 | 91.5 | 71.1 | 97.0 | 96.5 |
tuna fish can | 70.4 | 89.3 | 79.0 | 87.7 | 53.9 | 88.9 | 88.9 |
pudding box | 62.9 | 95.7 | 89.8 | 82.7 | 79.6 | 94.6 | 94.5 |
gelatin box | 75.2 | 96.1 | 96.2 | 91.9 | 32.1 | 96.9 | 92.7 |
potted meat can | 59.6 | 88.6 | 89.6 | 76.2 | 54.9 | 88.1 | 87.6 |
banana | 72.3 | 93.7 | 93.0 | 81.2 | 69.1 | 94.9 | 96.1 |
pitcher base | 52.5 | 96.5 | 94.2 | 90.1 | 40.4 | 96.9 | 96.7 |
bleach cleanser | 50.5 | 93.2 | 91.1 | 81.2 | 44.1 | 94.8 | 95.4 |
bowl | 69.6 | 90.2 | 95.5 | 81.4 | 0.9 | 96.3 | 92.0 |
mug | 57.7 | 95.4 | 93.0 | 81.4 | 39.2 | 94.2 | 94.4 |
power drill | 55.1 | 95.1 | 91.1 | 85.5 | 19.8 | 95.9 | 95.5 |
wood block | 31.8 | 90.4 | 94.3 | 81.9 | 27.9 | 92.6 | 92.8 |
scissors | 35.8 | 92.7 | 79.6 | 60.9 | 27.7 | 95.7 | 96.9 |
large marker | 58.0 | 91.8 | 92.8 | 75.6 | 74.2 | 89.1 | 89.4 |
large clamp | 25.0 | 93.6 | 95.9 | 74.3 | 34.7 | 96.8 | 95.9 |
extra large clamp | 15.8 | 88.4 | 95.8 | 73.3 | 10.1 | 96.0 | 97.1 |
foam brick | 40.4 | 96.8 | 96.1 | 81.9 | 45.8 | 97.3 | 97.7 |
Objects | PVNet [48] | PoseCNN [7] | DPOD [10] | Pix2Pose [6] | HybridPose [57] | Robust6D [39] | Ours |
---|---|---|---|---|---|---|---|
ape | 43.6 | 21.6 | 53.3 | 58.1 | 63.1 | 85.0 | 99.7 |
benchvise | 99.9 | 81.8 | 95.3 | 91.0 | 99.9 | 95.5 | 100.0 |
cam | 86.9 | 36.6 | 90.4 | 60.9 | 90.4 | 91.2 | 100.0 |
can | 95.5 | 68.8 | 94.1 | 84.4 | 98.5 | 95.1 | 99.1 |
cat | 79.3 | 41.8 | 60.4 | 65.0 | 89.4 | 93.6 | 100.0 |
driller | 96.4 | 63.5 | 97.7 | 76.3 | 98.5 | 82.6 | 100.0 |
duck | 52.6 | 27.2 | 66.0 | 43.8 | 65.0 | 88.1 | 100.0 |
eggbox | 99.2 | 69.6 | 99.7 | 96.8 | 100.0 | 99.9 | 98.9 |
glue | 95.7 | 80.0 | 93.8 | 79.4 | 98.8 | 99.6 | 99.4 |
holepuncher | 81.9 | 42.6 | 65.8 | 74.8 | 89.7 | 92.6 | 100.0 |
iron | 98.9 | 74.9 | 99.8 | 83.4 | 100.0 | 95.9 | 100.0 |
lamp | 99.3 | 71.1 | 88.1 | 82.0 | 99.5 | 94.4 | 99.8 |
phone | 92.4 | 47.7 | 74.2 | 45.0 | 94.9 | 93.5 | 99.9 |
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Feng, Q.; Nong, J.; Liang, Y. EdgePose: An Edge Attention Network for 6D Pose Estimation. Mathematics 2024, 12, 2607. https://doi.org/10.3390/math12172607
Feng Q, Nong J, Liang Y. EdgePose: An Edge Attention Network for 6D Pose Estimation. Mathematics. 2024; 12(17):2607. https://doi.org/10.3390/math12172607
Chicago/Turabian StyleFeng, Qi, Jian Nong, and Yanyan Liang. 2024. "EdgePose: An Edge Attention Network for 6D Pose Estimation" Mathematics 12, no. 17: 2607. https://doi.org/10.3390/math12172607