Research map / Point clouds / Shape and surface completion
PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use PC2WF instead of Harris3D + NMS | ABC vertex detection · ABC | mAPᵛ: 0.488 -> 0.894 | Helped |
| use PC2WF instead of Harris3D + NMS | Furniture vertex detection · Furniture | mAPᵛ: 0.480 -> 0.894 | Helped |
| use PC2WF instead of EC-Net | ABC edge detection · ABC | mAPᵉ: 0.751 -> 0.888 | Helped |
| use PC2WF instead of PolyFit | Furniture wireframe reconstruction · Furniture | msAP: 0.585 -> 0.897 | Helped |
| add NMS (ablation) | PC2WF · ABC | msAP: 0.494 -> 0.891 | Helped |
| add edge straightening (ablation) | PC2WF · ABC | msAP: 0.802 -> 0.891 | Helped |
| include both ground-truth and predicted-vertex edge sample sets (ablation) | PC2WF edge detector · ABC | msAP: 0.760 -> 0.891 | Helped |
| include ground-truth edge sample sets (ablation) | PC2WF edge detector · ABC | msAP: 0.740 -> 0.891 | Helped |
| include negative edge samples (ablation) | PC2WF edge detector · ABC | msAP: 0.586 -> 0.891 | Helped |
| increase input noise from 0 to 0.05 (ablation) | PC2WF · ABC | msAP: 0.947 -> 0.570 | Hurt |
Closest papers by meaning
- NEAT: Distilling 3D Wireframes from Neural Attraction Fields (CVPR 2024 · similarity 0.87)
- EdgeDiff: Edge-aware Diffusion Network for Building Reconstruction from Point Clouds (CVPR 2025 · similarity 0.87)
- A Lightweight UDF Learning Framework for 3D Reconstruction Based on Local Shape Functions (CVPR 2025 · similarity 0.86)
- EdgeMovingNet: Edge-preserving Point Cloud Reconstruction via Joint Geometry Features (CVPR 2025 · similarity 0.85)
- BWFormer: Building Wireframe Reconstruction from Airborne LiDAR Point Cloud with Transformer (CVPR 2025 · similarity 0.85)
- PBWR: Parametric-Building-Wireframe Reconstruction from Aerial LiDAR Point Clouds (CVPR 2024 · similarity 0.85)
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