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DOPER_BOP

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metadata 
license: apache-2.0
tags:
  - pose-estimation
  - bop
  - doper
  - keypoints
  - 6dof

DOPER BOP -- Per-Object 6DoF Pose Estimation on BOP Datasets

Per-object DOPER-t (keypoint) and RTMDet-tiny (detector) models for all 168 objects across 9 BOP datasets. Trained on synthetic PBR data only. Supports two evaluation modes:

  1. Keypoints-only (GT bounding box) -- upper bound on pose accuracy
  2. Detection + Pose (full pipeline, no GT) -- RTMDet detection, DOPER-t keypoints, PnP solve

Results Summary

Keypoints-Only (GT BBox)

Dataset Objects Split N GT ADD-AUC ADD-S-AUC MSSD-AUC MSPD-AUC ARMSSD ARMSPD
lm 15 test 3000 0.9820 0.9931 0.9733 0.9766 0.6166 0.7718
lmo 8 test 1517 0.9655 0.9798 0.9565 0.9786 0.5042 0.7479
tless 30 test_primesense 6900 0.6816 0.7030 0.6662 0.9140 0.2245 0.4098
tudl 3 test 600 0.9635 0.9789 0.9431 0.9587 0.3465 0.6038
ycbv 21 test 4125 0.8671 0.8884 0.8503 0.9177 0.2528 0.4584
hb 33 val_primesense 23120 0.9538 0.9630 0.9451 0.9772 0.5961 0.7718
itodd 28 val 123 0.6352 0.6582 0.6123 0.6237 0.0925 0.1037
icbin 2 test 2250 0.8641 0.8921 0.8372 0.9192 0.1548 0.3391
hope 28 val 920 0.8759 0.8937 0.8581 0.8878 0.3331 0.4424

Detection + Pose (Full Pipeline)

Per-object RTMDet-tiny detectors trained with ~30-40% negative images (cross-dataset).

Dataset Objects Split N GT Det Rate ADD-AUC ADD-S-AUC MSSD-AUC MSPD-AUC ARMSSD ARMSPD
lm 15 test 3000 97.4% 0.9843 0.9950 0.9768 0.9783 0.6264 0.7813
lmo 8 test 1517 94.5% 0.9701 0.9840 0.9622 0.9801 0.5234 0.7602
tless 30 test_primesense 6900 74.1% 0.7079 0.7292 0.6940 0.9129 0.2581 0.4351
tudl 3 test 600 93.8% 0.9064 0.9251 0.8831 0.9380 0.3032 0.5281
ycbv 21 test 4125 85.0% 0.7650 0.7882 0.7473 0.8695 0.2001 0.3671
itodd 28 val 123 66.7% 0.6369 0.6554 0.6202 0.5718 0.1609 0.1177
icbin 2 test 2250 9.1% 0.9116 0.9414 0.8826 0.8452 0.1506 0.2443
hope 28 val 920 57.8% 0.9199 0.9362 0.9065 0.8856 0.3605 0.4596

Det Rate = fraction of GT instances matched by a detection (IoU > 0.1). AUC metrics computed only over detected+solved instances.

Metrics

Metric Description
ADD Average Distance of model points (non-symmetric)
ADD-S Average Distance of closest model points (symmetric-aware, a.k.a. ADI)
MSSD Maximum Symmetry-Aware Surface Distance (BOP)
MSPD Maximum Symmetry-Aware Projection Distance (BOP)
AUC Area Under the recall-vs-threshold Curve (40 thresholds in (0, 10x diameter])
ARMSSD Average Recall at BOP thresholds: {0.05, 0.10, ..., 0.50} x diameter
ARMSPD Average Recall at BOP thresholds: {5, 10, ..., 50} pixels

Method

  1. 3D Keypoints: 17 symmetry-aware keypoints per object from BOP meshes + models_info.json
  2. Keypoint Training: DOPER-t (CSPNeXt-tiny, 256x256, 300 epochs) on projected keypoints from PBR renders
  3. Detector Training: RTMDet-tiny (CSPNeXt-tiny, COCO pretrained, 20 epochs) per object, with ~30-40% cross-dataset negative images
  4. Inference: RTMDet detects object bbox -> DOPER-t predicts 2D keypoints -> PnP+RANSAC solves 6DoF pose

Training Data

  • BOP core (lm, lmo, tless, tudl, ycbv, hb, itodd, icbin): BOP BlenderProc PBR (train_pbr)
  • HOPE: Custom BlenderProc synthetic (~40K images/object)

Symmetry Handling

Type Strategy
None Farthest-point sampling (FPS) on mesh surface
Discrete Keypoints in fundamental domain, replicated under symmetry transforms
Continuous Axial keypoints + equidistant ring perpendicular to symmetry axis

Per-Object Results (Keypoints-Only, GT BBox)

LM (15 objects, test)
ID Diam (mm) N GT ADD-AUC ADD-S-AUC MSSD-AUC MSPD-AUC ARMSSD ARMSPD
1 102.1 200 0.9990 1.0000 0.9986 1.0000 0.8260 0.9965
2 247.5 200 0.9926 0.9995 0.9779 0.9622 0.6405 0.6985
3 167.4 200 0.9106 0.9558 0.9161 0.9319 0.2665 0.4410
4 172.5 200 0.9936 0.9990 0.9922 0.9980 0.6900 0.9295
5 201.4 200 0.9955 0.9986 0.9915 0.9914 0.8490 0.9080
6 154.5 200 1.0000 1.0000 1.0000 1.0000 0.9285 0.9950
7 124.3 200 0.9335 0.9620 0.9018 0.9450 0.1750 0.4435
8 261.5 200 0.9977 0.9995 0.9936 0.9939 0.8770 0.9205
9 109.0 200 0.9749 0.9949 0.9527 0.9672 0.3800 0.6435
10 164.6 200 0.9911 0.9975 0.9875 0.9878 0.6850 0.8915
11 175.9 200 0.9851 0.9965 0.9730 0.9940 0.4825 0.8785
12 145.5 200 0.9734 0.9950 0.9521 0.9456 0.3175 0.5175
13 278.1 200 0.9982 0.9994 0.9970 0.9968 0.8965 0.9090
14 282.6 200 0.9878 0.9991 0.9695 0.9370 0.4830 0.4875
15 212.4 200 0.9972 1.0000 0.9957 0.9989 0.7515 0.9165
mean 0.9820 0.9931 0.9733 0.9766 0.6166 0.7718
LMO (8 objects, test)
ID Diam (mm) N GT ADD-AUC ADD-S-AUC MSSD-AUC MSPD-AUC ARMSSD ARMSPD
1 102.1 187 0.9828 0.9880 0.9790 0.9951 0.6567 0.8995
5 201.4 199 0.9935 0.9987 0.9861 0.9864 0.7111 0.8201
6 154.5 196 0.9858 0.9909 0.9811 0.9971 0.6143 0.9276
8 261.5 200 0.9981 0.9996 0.9949 0.9954 0.8350 0.8840
9 109.0 188 0.9608 0.9826 0.9368 0.9654 0.2883 0.5968
10 164.6 191 0.8719 0.9132 0.8753 0.9677 0.2435 0.6429
11 175.9 154 0.9625 0.9735 0.9524 0.9828 0.4422 0.7721
12 145.5 200 0.9689 0.9920 0.9468 0.9389 0.2430 0.4405
mean 0.9655 0.9798 0.9565 0.9786 0.5042 0.7479
T-LESS (30 objects, test_primesense)
ID Diam (mm) N GT ADD-AUC ADD-S-AUC MSSD-AUC MSPD-AUC ARMSSD ARMSPD
1 63.5 900 0.7947 0.8240 0.7741 0.9646 0.1030 0.6047
2 66.2 500 0.1058 0.1158 0.0997 0.9357 0.0000 0.3038
3 65.3 400 0.8078 0.8419 0.7850 0.9506 0.0793 0.4778
4 80.7 650 0.8712 0.8968 0.8537 0.9638 0.2065 0.6006
5 108.7 200 0.9694 0.9824 0.9635 0.9778 0.5290 0.7625
6 108.3 100 0.9235 0.9435 0.9133 0.9665 0.2870 0.6640
7 178.6 250 0.8267 0.8514 0.8046 0.8572 0.3076 0.3760
8 217.2 150 0.9177 0.9372 0.9043 0.8987 0.3347 0.4493
9 144.5 250 0.8684 0.9066 0.8286 0.7812 0.0184 0.0160
10 90.2 150 0.9417 0.9542 0.9355 0.9800 0.5987 0.8020
11 76.6 200 0.9243 0.9453 0.9088 0.9610 0.3530 0.6250
12 86.0 150 0.9362 0.9558 0.9187 0.9440 0.3640 0.5140
13 58.1 150 0.8567 0.8872 0.8393 0.9662 0.1493 0.6033
14 71.9 150 0.2485 0.2687 0.2363 0.9418 0.0000 0.3580
15 68.6 150 0.8617 0.8970 0.8390 0.9553 0.0887 0.5220
16 69.2 200 0.0229 0.0271 0.0217 0.9280 0.0000 0.2115
17 112.8 150 0.1552 0.1765 0.1473 0.8477 0.0013 0.0133
18 111.0 150 0.9515 0.9695 0.9283 0.9027 0.3733 0.4573
19 89.1 200 0.0000 0.0000 0.0000 0.8629 0.0000 0.0005
20 98.9 250 0.4074 0.4462 0.3760 0.8706 0.0000 0.0324
21 92.3 200 0.8999 0.9182 0.8811 0.9386 0.4730 0.6105
22 92.3 200 0.9441 0.9588 0.9289 0.9469 0.5480 0.6815
23 142.6 250 0.9696 0.9815 0.9635 0.9632 0.5932 0.7012
24 84.7 200 0.9069 0.9315 0.8935 0.9665 0.3010 0.6300
25 108.8 100 0.9378 0.9470 0.9335 0.9770 0.7100 0.8230
26 108.8 100 0.9030 0.9297 0.8672 0.8755 0.0720 0.1180
27 152.5 100 0.2220 0.2417 0.2095 0.7245 0.0000 0.0030
28 124.8 200 0.2886 0.3205 0.2627 0.8064 0.0000 0.0015
29 134.2 100 0.8878 0.9270 0.8812 0.8858 0.2430 0.3030
30 88.8 150 0.0967 0.1075 0.0882 0.8798 0.0000 0.0293
mean 0.6816 0.7030 0.6662 0.9140 0.2245 0.4098
TUD-L (3 objects, test)
ID Diam (mm) N GT ADD-AUC ADD-S-AUC MSSD-AUC MSPD-AUC ARMSSD ARMSPD
1 430.3 200 0.9711 0.9835 0.9545 0.9439 0.3925 0.5605
2 175.7 200 0.9324 0.9547 0.9096 0.9689 0.2365 0.6570
3 352.4 200 0.9870 0.9985 0.9651 0.9633 0.4105 0.5940
mean 0.9635 0.9789 0.9431 0.9587 0.3465 0.6038
YCB-V (21 objects, test)
ID Diam (mm) N GT ADD-AUC ADD-S-AUC MSSD-AUC MSPD-AUC ARMSSD ARMSPD
1 172.1 300 0.9468 0.9800 0.9470 0.9587 0.1273 0.5660
2 269.6 225 0.9967 0.9996 0.9943 0.9876 0.6609 0.7849
3 198.4 375 0.9925 0.9975 0.9906 0.9956 0.6568 0.8539
4 120.5 450 0.9518 0.9670 0.9436 0.9778 0.4098 0.7502
5 196.5 150 0.9512 0.9622 0.9373 0.9408 0.3940 0.5693
6 89.8 300 0.7816 0.8251 0.7290 0.8697 0.0230 0.1147
7 142.5 75 0.9780 0.9923 0.9743 0.9943 0.3600 0.8467
8 114.1 75 0.9910 1.0000 0.9863 1.0000 0.5920 0.9480
9 129.5 225 0.8569 0.8846 0.8296 0.9054 0.2316 0.4089
10 197.8 150 0.7247 0.7437 0.6943 0.9063 0.0400 0.2027
11 259.5 225 0.7109 0.7407 0.6770 0.6787 0.0262 0.0236
12 259.6 300 0.7168 0.7312 0.6992 0.8213 0.2130 0.2900
13 161.9 150 0.8455 0.8883 0.8307 0.8267 0.0587 0.0527
14 125.0 150 0.9562 0.9832 0.9252 0.9317 0.0500 0.3567
15 226.2 300 0.9940 0.9972 0.9878 0.9849 0.5987 0.7610
16 237.3 75 0.6860 0.7153 0.6660 0.8560 0.0000 0.0320
17 204.0 75 0.9407 0.9567 0.9337 0.9587 0.1373 0.5240
18 121.4 150 0.9830 0.9893 0.9788 0.9983 0.5080 0.9033
19 174.7 150 0.9033 0.9292 0.8903 0.9232 0.0753 0.4100
20 217.1 150 0.9152 0.9480 0.8945 0.9010 0.1467 0.2180
21 102.9 75 0.3873 0.4257 0.3460 0.8553 0.0000 0.0093
mean 0.8671 0.8884 0.8503 0.9177 0.2528 0.4584
HB (33 objects, val_primesense)
ID Diam (mm) N GT ADD-AUC ADD-S-AUC MSSD-AUC
1 232.6 680 0.9906 0.9962 0.9803
2 257.4 340 1.0000 1.0000 1.0000
3 166.5 1020 0.9737 0.9834 0.9640
4 179.0 680 0.9849 0.9908 0.9793
5 205.4 680 0.9864 0.9899 0.9825
6 121.4 340 0.9741 0.9968 0.9471
7 263.7 340 0.9984 1.0000 0.9940
8 186.8 680 0.9931 0.9968 0.9903
9 166.6 680 0.9139 0.9348 0.8892
10 180.8 680 0.9338 0.9546 0.9307
11 238.5 340 0.9153 0.9433 0.9260
12 156.9 1360 0.9688 0.9823 0.9537
13 145.3 1020 0.9582 0.9733 0.9455
14 243.7 680 0.9922 0.9965 0.9857
15 113.0 1700 0.9483 0.9611 0.9374
16 101.6 1020 0.9519 0.9679 0.9296
17 132.8 1360 0.9448 0.9571 0.9293
18 211.1 680 0.1994 0.2258 0.1693
19 185.6 680 0.9987 0.9993 0.9982
20 244.8 340 1.0000 1.0000 1.0000
21 212.6 340 0.9979 0.9993 0.9963
22 190.2 1360 0.9874 0.9933 0.9823
23 233.9 1020 0.9987 0.9997 0.9971
24 252.3 340 0.9940 0.9999 0.9889
25 202.9 680 0.9753 0.9853 0.9612
26 183.8 680 0.9785 0.9893 0.9613
27 264.4 340 0.9989 0.9998 0.9983
28 477.5 340 1.0000 1.0000 1.0000
29 198.0 680 0.9720 0.9970 0.9489
30 416.2 340 1.0000 1.0000 1.0000
31 158.0 340 0.9912 0.9971 0.9817
32 201.8 680 0.9610 0.9701 0.9487
33 187.2 680 0.9945 0.9974 0.9916
mean 0.9538 0.9630 0.9451
ITODD (28 objects, val)
ID Diam (mm) N GT ADD-AUC ADD-S-AUC MSSD-AUC MSPD-AUC ARMSSD ARMSPD
1 64.1 4 0.9125 0.9437 0.8562 0.5500 0.0000 0.0000
2 51.5 3 0.4833 0.5000 0.4500 0.7167 0.0000 0.0000
3 142.2 4 0.0000 0.0000 0.0000 0.2750 0.0000 0.0000
4 139.4 3 0.2583 0.3000 0.2250 0.3083 0.0000 0.0000
5 158.6 6 0.7167 0.7583 0.6833 0.2500 0.0000 0.0000
6 85.3 5 0.9650 0.9750 0.9650 0.9750 0.1800 0.6000
7 38.5 5 0.0000 0.0200 0.0000 0.8250 0.0000 0.0000
8 68.9 3 0.8417 0.8917 0.7917 0.5083 0.0000 0.0000
9 94.8 3 0.0000 0.0000 0.0000 0.4000 0.0000 0.0000
10 55.7 4 0.9563 0.9688 0.9313 0.8938 0.0000 0.1500
11 140.1 5 0.5950 0.6300 0.5850 0.4550 0.0600 0.0000
12 107.7 4 0.0000 0.0000 0.0000 0.1437 0.0000 0.0000
13 128.1 4 0.9750 0.9875 0.9563 0.7438 0.1750 0.1000
14 102.9 3 0.0000 0.0000 0.0000 0.3917 0.0000 0.0000
15 114.2 3 0.9583 0.9833 0.9167 0.4750 0.0333 0.0000
16 193.1 3 0.9833 1.0000 0.9667 0.5917 0.2333 0.0000
17 77.8 3 0.7333 0.7833 0.6833 0.6167 0.0000 0.0000
18 108.5 3 0.2083 0.2417 0.1917 0.6583 0.0000 0.0000
19 121.4 3 0.7917 0.8417 0.7500 0.5083 0.0000 0.0000
20 122.0 4 0.9812 0.9875 0.9750 0.9062 0.6250 0.4250
21 171.2 3 0.9917 1.0000 0.9833 0.7833 0.5333 0.1333
22 267.5 3 0.9333 0.9667 0.8833 0.3333 0.0000 0.0000
23 56.9 1 1.0000 1.0000 0.9750 0.9750 0.4000 0.7000
24 65.0 6 0.3333 0.3583 0.3292 0.8708 0.0000 0.0000
25 48.5 6 0.9375 0.9625 0.9083 0.7917 0.0000 0.3000
26 66.8 6 0.9792 0.9875 0.9667 0.9458 0.3500 0.4333
27 55.7 5 0.7200 0.7750 0.6700 0.6800 0.0000 0.0000
28 24.1 18 0.5306 0.5681 0.5014 0.8917 0.0000 0.0611
mean 0.6352 0.6582 0.6123 0.6237 0.0925 0.1037
IC-BIN (2 objects, test)
ID Diam (mm) N GT ADD-AUC ADD-S-AUC MSSD-AUC MSPD-AUC ARMSSD ARMSPD
1 136.6 1800 0.8560 0.8871 0.8380 0.9422 0.2424 0.4592
2 220.6 450 0.8723 0.8971 0.8364 0.8962 0.0671 0.2191
mean 0.8641 0.8921 0.8372 0.9192 0.1548 0.3391
HOPE (28 objects, val)
ID Diam (mm) N GT ADD-AUC ADD-S-AUC MSSD-AUC MSPD-AUC ARMSSD ARMSPD
1 107.9 45 0.8717 0.8867 0.8644 0.9417 0.4178 0.5844
2 153.9 45 0.8994 0.9150 0.8733 0.8478 0.3133 0.4000
3 115.0 45 0.8622 0.8778 0.8533 0.9439 0.4378 0.6200
4 89.6 20 0.9525 0.9713 0.9400 0.9475 0.3050 0.5300
5 98.0 30 0.8350 0.8458 0.8250 0.9417 0.5300 0.6633
6 208.7 25 0.9910 0.9960 0.9880 0.9370 0.5920 0.4880
7 89.6 40 0.8325 0.8663 0.8150 0.9187 0.1250 0.3600
8 115.6 25 0.8990 0.9190 0.8780 0.8470 0.2760 0.3600
9 206.0 25 0.8610 0.8910 0.8210 0.7250 0.2000 0.1880
10 89.7 35 0.6257 0.6493 0.5979 0.8479 0.1571 0.2286
11 153.9 40 0.9238 0.9338 0.9094 0.9256 0.5225 0.6525
12 207.1 50 0.9535 0.9710 0.9310 0.7805 0.3500 0.2800
13 153.4 30 0.9767 0.9917 0.9550 0.8525 0.3767 0.3733
14 204.5 25 0.9660 0.9760 0.9500 0.8920 0.4120 0.3520
15 75.7 35 0.8814 0.9150 0.8521 0.8929 0.0657 0.3943
16 161.5 50 0.9560 0.9660 0.9420 0.8980 0.5120 0.5220
17 205.7 25 0.7150 0.7290 0.6790 0.7060 0.1960 0.1840
18 122.8 30 0.9658 0.9783 0.9458 0.9133 0.4167 0.4467
19 89.2 20 0.9650 0.9800 0.9550 0.9750 0.3600 0.7350
20 89.9 30 0.7575 0.7900 0.7350 0.9050 0.0900 0.3400
21 89.2 55 0.8286 0.8509 0.8141 0.9223 0.2855 0.5655
22 152.4 20 0.7838 0.8137 0.7475 0.8462 0.0650 0.1350
23 151.3 40 0.8206 0.8475 0.7900 0.7162 0.0975 0.1500
24 151.3 10 1.0000 1.0000 0.9975 0.9750 0.8000 0.6500
25 252.8 35 0.9786 0.9836 0.9714 0.9257 0.5286 0.5400
26 107.1 35 0.7529 0.7679 0.7457 0.9364 0.3771 0.5800
27 76.1 35 0.7736 0.7971 0.7636 0.9443 0.1629 0.4886
28 82.9 20 0.8962 0.9150 0.8875 0.9525 0.3550 0.5750
mean 0.8759 0.8937 0.8581 0.8878 0.3331 0.4424

File Structure 

{dataset}/obj_{NNNNNN}/
  best_coco_AP_epoch_NNN.pth   # DOPER-t keypoint checkpoint
  keypoints_3d.json             # 17 symmetry-aware 3D keypoints (mm)
  bop_summary.json              # Evaluation metrics
  vis_grid.jpg                  # Qualitative results (GT bbox eval)

Detector checkpoints and BOP submission CSVs are in the bop_submission/ directory.

Usage 

from huggingface_hub import hf_hub_download

# Download keypoint model + 3D keypoints for ycbv object 1
ckpt = hf_hub_download("TontonTremblay/DOPER_BOP", "ycbv/obj_000001/best_coco_AP_epoch_200.pth", repo_type="dataset")
kpts = hf_hub_download("TontonTremblay/DOPER_BOP", "ycbv/obj_000001/keypoints_3d.json", repo_type="dataset")

BOP Submission

Pre-computed detection+pose results for all 9 datasets in BOP CSV format:

  • bop_submission/doper-t_bop_results_det_v2.zip -- RTMDet + DOPER-t + PnP (no GT bbox)

Citation

If you use these models, please cite the DOPER project and the BOP benchmark.