Research map / Point clouds / Shape and surface completion
Position-Aware Guided Point Cloud Completion with CLIP Model
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Expands unimodal point cloud completion into a multimodal framework by generating text and six projection images per shape (PCN-TI, MVP-TI) and adding a CLIP-based position-aware module with block-wise weighted maps.
Problem
Point cloud completion from partial scans lacks fine-grained localization of missing regions; existing multimodal methods need calibrated images or LLM-generated text, which is costly.
Approach
Build Point-Text-Image triplets from existing completion datasets using keyword-substituted category text and six orthogonal projection maps, then use CLIP with a position-aware module that learns per-block weights over projection images plus global inpainted projections to localize missing parts.
Key results
- On PCN, the method reaches an average L1 CD of 6.34 (x10^3), best among compared methods including CRA-PCN at 6.39.
- On MVP validation, average L2 CD of 5.32 and F1-Score@1% of 0.529, matching or slightly beating CRA-PCN (5.33, 0.529).
- On KITTI, MMD of 0.574 versus 0.967 for SVDFormer and 1.179 for SeedFormer.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the CLIP-enhanced and position-aware modules | CRA-PCN · PCN | L1 CD × 10^3: 6.39 -> 6.34 | Helped |
| Add the CLIP-enhanced and position-aware modules | CRA-PCN · MVP validation set | L2 CD (Ave): 5.33 -> 5.32 | Helped |
| Add the CLIP-enhanced and position-aware modules | CRA-PCN · MVP validation set | F1-Score@1%: 0.529 -> 0.529 | No clear effect |
| Add the proposed method | AdaPoinTr baseline · KITTI | MMD: 0.967 -> 0.574 | Helped |
| Add the position-aware module to the CLIP-enhanced model (ablation) | SnowflakeNet · PCN | L1 CD × 10^3: 7.19 -> 7.17 | Helped |
| Add the position-aware module to the CLIP-enhanced model (ablation) | PoinTr · PCN | L1 CD × 10^3: 7.26 -> 7.20 | Helped |
| Add the position-aware module to the CLIP-enhanced model (ablation) | AdaPoinTr · PCN | L1 CD × 10^3: 6.49 -> 6.48 | Helped |
| Add the position-aware module to the CLIP-enhanced model (ablation) | CRA-PCN · PCN | L1 CD × 10^3: 6.37 -> 6.34 | Helped |
| Use the complete point cloud projection map instead of the incomplete projection map (ablation) | CRA-PCN + CE + PA · PCN | L1 CD × 10^3: 6.34 -> 6.11 | Helped |
| Use a BERT-based text encoder instead of the text transformer encoder (ablation) | CLIP-enhanced module · PCN | L1 CD × 10^3 | No clear effect |
Surprising findings
- Replacing the incomplete projection map with the complete one (CRA-PCN-GT) improves L1 CD to 6.11, showing a large headroom from global positional information.
Limitations
- Text descriptions are only category names and do not encode missing-part location, limiting the gain from text.
- Gains over strong baselines are small (e.g., 6.39 to 6.34 on PCN; equal F1 on MVP).
- Only single-object datasets with projection images; no evaluation of real-world multimodal inputs beyond KITTI MMD.
Experimental setup
Datasets: PCN MVP ShapeNet-ViPC KITTI
Benchmarks: PCN MVP KITTI
Metrics: L1 Chamfer Distance L2 Chamfer Distance F1-Score@1% MMD Fidelity
Baselines: FoldingNet TopNet PCN GRNet PoinTr SnowflakeNet FBNet ProxyFormer SeedFormer AnchorFormer AdaPoinTr ODGNet
Builds on: CLIP CRA-PCN PoinTr SnowflakeNet AdaPoinTr
Closest papers by meaning
- Point-based Instance Completion with Scene Constraints (ICLR 2025 · similarity 0.88)
- PCDreamer: Point Cloud Completion Through Multi-view Diffusion Priors (CVPR 2025 · similarity 0.88)
- DMF-Net: Image-Guided Point Cloud Completion with Dual-Channel Modality Fusion and Shape-Aware Upsampling Transformer (AAAI 2025 · similarity 0.88)
- DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided Quantization (AAAI 2025 · similarity 0.88)
- Orthogonal Dictionary Guided Shape Completion Network for Point Cloud (AAAI 2024 · similarity 0.87)
- SymmCompletion: High-Fidelity and High-Consistency Point Cloud Completion with Symmetry Guidance (AAAI 2025 · similarity 0.87)
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