Research map / Point clouds / Segmentation and detection
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace NMS with aggregation of multiple proposals by position (ablation) | 3D-MPA · ScanNetV2 val | mAP: 47.5 -> 52.4; +4.9 mAP | Helped |
| Use learned embedding features rather than proposal positions for aggregation (ablation) | 3D-MPA · ScanNetV2 val | mAP: 52.4 -> 56.7 | Helped |
| Use learned geometric features rather than proposal positions for aggregation (ablation) | 3D-MPA · ScanNetV2 val | mAP: 52.4 -> 57.8; +5.4 mAP | Helped |
| Use geometric rather than embedding aggregation features (ablation) | 3D-MPA · ScanNetV2 val | mAP: 56.7 -> 57.8; +1.1 mAP | Helped |
| Add a 10-layer GCN for proposal consolidation (ablation) | 3D-MPA with geometric aggregation features · ScanNetV2 val | mAP: 57.8 -> 59.1; +1.3 mAP | Helped |
| Replace NMS with multi-proposal aggregation and add the GCN (ablation) | 3D-MPA · ScanNetV2 val | mAP: 47.5 -> 59.1; +11.6 mAP | Helped |
| Sample proposal positions randomly instead of using Farthest Point Sampling | 3D-MPA · 3D point-cloud proposal generation | proposal quality | Helped |
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