6D Pose Estimation On Linemod
평가 지표
Accuracy
Mean ADD
Mean IoU
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Accuracy | Mean ADD | Mean IoU |
|---|---|---|---|
| real-time-seamless-single-shot-6d-object-pose | 90.37% | 55.95 | 99.92 |
| bpnp-further-empowering-end-to-end-learning | 99.21% | - | - |
| pvnet-pixel-wise-voting-network-for-6dof-pose | 99% | 86.27 | - |
| repose-real-time-iterative-rendering-and | - | 96.1 | - |
| cullnet-calibrated-and-pose-aware-confidence | 97.7% | 78.3 | - |
| cdpn-coordinates-based-disentangled-pose | 98.1% | 89.86 | - |
| bb8-a-scalable-accurate-robust-to-partial | 83.9% | 43.6 | - |
| end-to-end-differentiable-6dof-object-pose | - | 86.8 | - |
| epro-pnp-generalized-end-to-end-probabilistic-1 | - | 96.36 | - |
| efficientpose-an-efficient-accurate-and | - | 97.35 | - |
| bpnp-further-empowering-end-to-end-learning | - | 93.3 | - |
| estimating-6d-pose-from-localizing-designated | 94.5% | 72.6 | - |
| ssd-6d-making-rgb-based-3d-detection-and-6d | - | 76.3 | 99.4 |
| pix2pose-pixel-wise-coordinate-regression-of | - | 32 | - |
| rnnpose-recurrent-6-dof-object-pose | - | 97.37 | - |
| epro-pnp-generalized-end-to-end-probabilistic | - | 95.8 | - |
| implicit-3d-orientation-learning-for-6d | - | 28.7 | - |
| dpod-dense-6d-pose-object-detector-in-rgb | - | 95.2 | - |
| occlusion-robust-object-pose-estimation-with | - | 95.61 | - |
| yolov5-6d-advancing-6-dof-instrument-pose | - | 96.84 | - |
| deepim-deep-iterative-matching-for-6d-pose | 97.5 | 88.6 | - |
| hybridpose-6d-object-pose-estimation-under | - | 91.3 | - |