Orientation estimation on T-LESS (ViVo)
12.7ARCSundermeyer et al. (2020)
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| Sundermeyer et al. (2020)Training=RGB: R,S, Test=RGBD, Scope=dataset, Model=CAD, Symm.=network2026.04 | 12.7 | 59.7 | 64.7 | 0.531 | |
| Hodaň et al. (2015)Training=templates, Test=RGBD, Scope=unknown, Model=CAD, Symm.=evaluation2026.04 | 14.8 | 61 | 65.4 | 80.1 | |
| Drost et al. (2010)Training=templates, Test=D, Scope=unknown, Model=reconstr., Symm.=repres.2026.04 | 15.5 | 60.9 | 63.8 | 9.2 | |
| ZTE PPF (2022)Training=templates, Test=D, Scope=unknown, Model=reconstr., Symm.=unknown2026.04 | 19.3 | 75.1 | 74.5 | 0.846 | |
| Vidal et al. (2018)Training=templates, Test=D, Scope=unknown, Model=reconstr., Symm.=repres.2026.04 | 19.5 | 68.9 | 71.5 | 7.06 | |
| Castro and Kim (2023)Training=RGB: S, Test=RGB, Scope=dataset, Model=CAD, Symm.=loss2026.04 | 23.2 | 80.5 | 80.9 | 0.059 | |
| ModalOcc.depth (2024)Training=RGBD: S, Test=D, Scope=unknown, Model=CAD, Symm.=unknown2026.04 | 23.6 | 80.6 | 82 | 4.72 | |
| Cai et al. (2022)Training=RGB: S, Test=RGB, Scope=dataset, Model=✗, Symm.=network2026.04 | 27.4 | 87.1 | 86 | 50.8 | |
| SARR–RGBTraining=RGB: R, Test=RGB, Scope=symmetry, Model=✗, Symm.=repres.2026.04 | 28.9 | 42.1 | 53.3 | 0.077 | |
| ModalOcc.rgbd (2024)Training=RGBD: S, Test=RGBD, Scope=unknown, Model=CAD, Symm.=unknown2026.04 | 29.3 | 94.1 | 87.6 | 7.24 | |
| Liu et al. (2025)Training=RGBD: R,S, Test=RGBD, Scope=object, Model=reconstr., Symm.=loss2026.04 | 29.6 | 95.1 | 86.8 | 2.48 | |
| Liu et al. (2025)Training=RGB: R,S, Test=RGB, Scope=object, Model=reconstr., Symm.=loss2026.04 | 30.2 | 93.4 | 85.6 | 0.214 | |
| ModalOcc.rgb (2024)Training=RGBD: S, Test=RGB, Scope=unknown, Model=CAD, Symm.=unknown2026.04 | 30.3 | 93.1 | 88.3 | 7.75 | |
| SARR–RGBTraining=RGB: R, Test=RGB, Scope=object, Model=✗, Symm.=repres.2026.04 | 30.8 | 45.8 | 58.8 | 0.077 | |
| Wang et al. (2021)Training=RGBD: R,S, Test=RGBD, Scope=object, Model=CAD, Symm.=loss2026.04 | 30.9 | 91.2 | 87.3 | 6.63 | |
| Su et al. (2022)Training=RGBD: R,S, Test=RGBD, Scope=object, Model=reconstr., Symm.=data2026.04 | 36.7 | 90.6 | 87.2 | 2.62 | |
| SARR–RGBTraining=RGB: R, Test=RGB, Scope=dataset*, Model=✗, Symm.=repres.2026.04 | 41.1 | 58.7 | 67.4 | 0.08 | |
| SARR–DepthTraining=D: R, Test=D, Scope=object, Model=✗, Symm.=repres.2026.04 | 41.9 | 58.1 | 67 | 0.077 | |
| SARR–DepthTraining=D: R, Test=D, Scope=dataset*, Model=✗, Symm.=repres.2026.04 | 42.9 | 59 | 66.7 | 0.077 | |
| SARR–RGBTraining=RGB: R, Test=RGB, Scope=dataset, Model=✗, Symm.=repres.2026.04 | 43.6 | 58.7 | 69.1 | 0.078 | |
| SARR–DepthTraining=D: R, Test=D, Scope=symmetry, Model=✗, Symm.=repres.2026.04 | 44 | 60.1 | 69.4 | 0.078 | |
| SARR–DepthTraining=D: R, Test=D, Scope=dataset, Model=✗, Symm.=repres.2026.04 | 44.8 | 60 | 69.4 | 0.074 |