Scene-level reconstruction on synthetic indoor scene dataset
83.7IoUDynamic Plane Convolutional Occupancy Networks
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| Dynamic Plane Convolutional Occupancy Networkspositional encoding=Without, planes=3 Canonical + 2 Dynamic2020.11 | 83.7 | 0.042 | 91 | 95.8 | |
| Dynamic Plane Convolutional Occupancy Networkspositional encoding=Without, planes=3 Canonical + 4 Dynamic2020.11 | 83.1 | 0.044 | 90.6 | 95.3 | |
| Dynamic Plane Convolutional Occupancy Networkspositional encoding=With, planes=3 Canonical + 4 Dynamic2020.11 | 83.1 | 0.043 | 91 | 95.6 | |
| Dynamic Plane Convolutional Occupancy Networkspositional encoding=With, planes=7 Dynamic2020.11 | 81.9 | 0.043 | 91 | 95.7 | |
| ConvONetpositional encoding=Without, planes=3 Canonical, grid resolution=32^32020.11 | 81.6 | 0.044 | 90.5 | 95.2 | |
| Dynamic Plane Convolutional Occupancy Networkspositional encoding=With, planes=3 Dynamic2020.11 | 81.4 | 0.042 | 91 | 95.8 | |
| Dynamic Plane Convolutional Occupancy Networkspositional encoding=Without, planes=7 Dynamic2020.11 | 81 | 0.042 | 90.9 | 95.7 | |
| Dynamic Plane Convolutional Occupancy Networkspositional encoding=With, planes=5 Dynamic2020.11 | 80 | 0.042 | 91.2 | 96 | |
| ConvONetpositional encoding=With, planes=3 Canonical2020.11 | 79.7 | 0.046 | 90.2 | 94.6 | |
| Dynamic Plane Convolutional Occupancy Networkspositional encoding=With, planes=3 Canonical + 2 Dynamic2020.11 | 79.7 | 0.043 | 90.8 | 95.9 | |
| Dynamic Plane Convolutional Occupancy Networkspositional encoding=Without, planes=3 Dynamic2020.11 | 79.5 | 0.043 | 90.7 | 95.4 | |
| Dynamic Plane Convolutional Occupancy Networkspositional encoding=Without, planes=5 Dynamic2020.11 | 79.1 | 0.043 | 90.5 | 95.5 | |
| ConvONetpositional encoding=Without, planes=3 Canonical2020.11 | 78.9 | 0.044 | 90.2 | 95 | |
| ONetpositional encoding=Without2020.11 | 47.5 | 0.203 | 78.3 | 54.1 |