Research map / 3D vision / Face and shape
Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-Resolution Model for Multi-Class Volumetric Labeling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use adaptive octree refinement instead of a fixed voxel grid | Semantic 3D reconstruction · Scene 1 and Scene 2 | overall accuracy: 92.3 -> 92.8; 83.4 -> 83.9 | No clear effect |
| Use adaptive octree refinement instead of a fixed voxel grid | Semantic 3D reconstruction · Scenes 1–4, 0.4 m | runtime: 430545 -> 19883; 416771 -> 19672; 91982 -> 5488; 92893 -> 4984 | Helped |
| Use adaptive octree refinement instead of a fixed voxel grid | Semantic 3D reconstruction · Scenes 1–4, 0.4 m | memory: 54.3 -> 2.7; 54.3 -> 2.6; 13.6 -> 0.7; 13.6 -> 0.7 | Helped |
| Use adaptive octree refinement instead of a fixed voxel grid | Semantic 3D reconstruction · Scenes 3 and 4, 0.2 m | memory: 108.5 -> 3.3; 108.5 -> 2.7 | Helped |
| Use class-specific geometric priors rather than generic surface-area regularization | 3D surface reconstruction · Enschede scenes | 3D surface shape quality | Helped |
Closest papers by meaning
- SceneCode: Monocular Dense Semantic Reconstruction Using Learned Encoded Scene Representations (CVPR 2019 · similarity 0.90)
- Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning (CVPR 2024 · similarity 0.89)
- Geometry and Uncertainty-Aware 3D Point Cloud Class-Incremental Semantic Segmentation (CVPR 2023 · similarity 0.89)
- Large Spatial Model: End-to-end Unposed Images to Semantic 3D (NeurIPS 2024 · similarity 0.88)
- MESC-3D:Mining Effective Semantic Cues for 3D Reconstruction from a Single Image (CVPR 2025 · similarity 0.88)
- Geometry-Guided Semantic Reconstruction for 3D Instance Segmentation (NeurIPS 2026 · similarity 0.88)
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