Research map / Point clouds / Shape and surface completion
Point Cloud Semantic Scene Completion from RGB-D Images
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use PCSSC-Net instead of DDRNet | semantic scene completion · Synthetic indoor scene dataset | CD: 1.87 -> 1.58 | Helped |
| use PCSSC-Net instead of MSN | point cloud completion · Synthetic indoor scene dataset | CD: 2.26 -> 1.58 | Helped |
| use PCSSC-Net instead of PointNet++ | point cloud semantic segmentation · Synthetic indoor scene dataset | mIoU: 87.4 -> 88.2 | Helped |
| add the semantic segmentation stream to the SGC decoder (ablation) | PCSSC-Net · Synthetic indoor scene dataset | CD: 1.68 -> 1.58 | Helped |
| add semantics-based CD terms to the loss (ablation) | PCSSC-Net · Synthetic indoor scene dataset | CD: 1.62 -> 1.58 | Helped |
Closest papers by meaning
- CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion (AAAI 2023 · similarity 0.92)
- Learning Local Displacements for Point Cloud Completion (CVPR 2022 · similarity 0.91)
- Semantic Scene Completion via Integrating Instances and Scene In-the-Loop (CVPR 2021 · similarity 0.91)
- Point-based Instance Completion with Scene Constraints (ICLR 2025 · similarity 0.88)
- DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided Quantization (AAAI 2025 · similarity 0.88)
- Context and Geometry Aware Voxel Transformer for Semantic Scene Completion (NeurIPS 2024 · similarity 0.88)
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