README.md

---
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

<!-- ## Demo: Full Pipeline (Detection + Keypoints)

![DOPER-t detection + keypoints on HOPE val](demo/doper_hope_det_keypoints.png)

*28 per-object RTMDet detectors + 28 per-object DOPER-t keypoint models running on a single HOPE val image. No GT bounding boxes.*
 -->
## Results Summary

### Keypoints-Only (GT BBox)

| Dataset | Objects | Split | N GT | ADD-AUC | ADD-S-AUC | MSSD-AUC | MSPD-AUC | AR<sub>MSSD</sub> | AR<sub>MSPD</sub> |
|---------|---------|-------|------|---------|-----------|----------|----------|-----------|-----------|
| **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 | AR<sub>MSSD</sub> | AR<sub>MSPD</sub> |
|---------|---------|-------|------|----------|---------|-----------|----------|----------|-----------|-----------|
| **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](http://bop.felk.cvut.cz/challenges/bop-challenge-2019/)) |
| **MSPD** | Maximum Symmetry-Aware Projection Distance ([BOP](http://bop.felk.cvut.cz/challenges/bop-challenge-2019/)) |
| **AUC** | Area Under the recall-vs-threshold Curve (40 thresholds in (0, 10x diameter]) |
| **AR<sub>MSSD</sub>** | Average Recall at BOP thresholds: {0.05, 0.10, ..., 0.50} x diameter |
| **AR<sub>MSPD</sub>** | 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)

<details>
<summary><b>LM</b> (15 objects, test)</summary>

| ID | Diam (mm) | N GT | ADD-AUC | ADD-S-AUC | MSSD-AUC | MSPD-AUC | AR<sub>MSSD</sub> | AR<sub>MSPD</sub> |
|----|-----------|------|---------|-----------|----------|----------|-----------|-----------|
| 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** |

</details>

<details>
<summary><b>LMO</b> (8 objects, test)</summary>

| ID | Diam (mm) | N GT | ADD-AUC | ADD-S-AUC | MSSD-AUC | MSPD-AUC | AR<sub>MSSD</sub> | AR<sub>MSPD</sub> |
|----|-----------|------|---------|-----------|----------|----------|-----------|-----------|
| 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** |

</details>

<details>
<summary><b>T-LESS</b> (30 objects, test_primesense)</summary>

| ID | Diam (mm) | N GT | ADD-AUC | ADD-S-AUC | MSSD-AUC | MSPD-AUC | AR<sub>MSSD</sub> | AR<sub>MSPD</sub> |
|----|-----------|------|---------|-----------|----------|----------|-----------|-----------|
| 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** |

</details>

<details>
<summary><b>TUD-L</b> (3 objects, test)</summary>

| ID | Diam (mm) | N GT | ADD-AUC | ADD-S-AUC | MSSD-AUC | MSPD-AUC | AR<sub>MSSD</sub> | AR<sub>MSPD</sub> |
|----|-----------|------|---------|-----------|----------|----------|-----------|-----------|
| 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** |

</details>

<details>
<summary><b>YCB-V</b> (21 objects, test)</summary>

| ID | Diam (mm) | N GT | ADD-AUC | ADD-S-AUC | MSSD-AUC | MSPD-AUC | AR<sub>MSSD</sub> | AR<sub>MSPD</sub> |
|----|-----------|------|---------|-----------|----------|----------|-----------|-----------|
| 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** |

</details>

<details>
<summary><b>HB</b> (33 objects, val_primesense)</summary>

| 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** |

</details>

<details>
<summary><b>ITODD</b> (28 objects, val)</summary>

| ID | Diam (mm) | N GT | ADD-AUC | ADD-S-AUC | MSSD-AUC | MSPD-AUC | AR<sub>MSSD</sub> | AR<sub>MSPD</sub> |
|----|-----------|------|---------|-----------|----------|----------|-----------|-----------|
| 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** |

</details>

<details>
<summary><b>IC-BIN</b> (2 objects, test)</summary>

| ID | Diam (mm) | N GT | ADD-AUC | ADD-S-AUC | MSSD-AUC | MSPD-AUC | AR<sub>MSSD</sub> | AR<sub>MSPD</sub> |
|----|-----------|------|---------|-----------|----------|----------|-----------|-----------|
| 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** |

</details>

<details>
<summary><b>HOPE</b> (28 objects, val)</summary>

| ID | Diam (mm) | N GT | ADD-AUC | ADD-S-AUC | MSSD-AUC | MSPD-AUC | AR<sub>MSSD</sub> | AR<sub>MSPD</sub> |
|----|-----------|------|---------|-----------|----------|----------|-----------|-----------|
| 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** |

</details>

## 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

```python
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](https://bop.felk.cvut.cz/).