Research map / Point clouds / Shape and surface completion
Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction From Raw Point Clouds
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed geometric initialization instead of random initialization (ablation) | SAIL-S3 · Shape reconstruction | surface quality | Helped |
| Use local sign flipping rather than switching it off (ablation) | SAIL-S3 · Shape reconstruction | surface quality | Helped |
| Use weighted interpolation of local fields instead of max pooling or average pooling (ablation) | SAIL-S3 · Shape reconstruction | surface quality | Mixed |
| Use SAIL-S3 rather than P2S | ThreeDScans noisy point clouds · ThreeDScans CD@0.01 | CD: 0.008 -> 0.004 | Helped |
| Use SAIL-S3 rather than P2S | ThreeDScans noisy point clouds · ThreeDScans CD@0.005 | CD: 0.008 -> 0.003 | Helped |
| Use SAIL-S3 rather than SPSR | ThreeDScans noisy point clouds · ThreeDScans CD@0.001 | CD: 0.003 -> 0.003 | No clear effect |
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