Research map / Point clouds / Point cloud classification
Cloud2Curve: Generation and Vectorization of Parametric Sketches
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add MSE loss to SWD (ablation) | Cloud2Curve vectorization · Quick, Draw! | test loss: 0.00123 -> 0.00034 | Helped |
| add MSE loss to SWD (ablation) | Cloud2Curve vectorization · KMNIST | test loss: 8.4\cdot 10^{-3} -> 2.1\cdot 10^{-4} | Helped |
| increase λd from 10⁻³ to 10⁻² (ablation) | Cloud2Curve vectorization · Quick, Draw! | classification accuracy: 0.80 -> 0.69 | Hurt |
| increase λd from 10⁻³ to 10⁻² (ablation) | Cloud2Curve vectorization · KMNIST | classification accuracy: 0.82 -> 0.73 | Hurt |
| use point-cloud rather than sequence supervision | BézierSketch · Quick, Draw! | classification accuracy: 0.86 -> 0.41 | Hurt |
| use Cloud2Curve point-cloud vectorization instead of BézierSketch with cloud data | Quick, Draw! vectorization · Quick, Draw! | classification accuracy: 0.41 -> 0.80 | Helped |
Closest papers by meaning
- SketchINR: A First Look into Sketches as Implicit Neural Representations (CVPR 2024 · similarity 0.90)
- Modelling complex vector drawings with stroke-clouds (ICLR 2024 · similarity 0.89)
- SketchRevive: Fine-Grained Pixel-to-Vector Sketch Completion with Diffusion-Prior-Guided Multimodal LLMs (CVPR 2026 · similarity 0.87)
- StrokeFusion: Vector Sketch Generation via Joint Stroke-UDF Encoding and Latent Sequence Diffusion (AAAI 2026 · similarity 0.86)
- Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch (CVPR 2025 · similarity 0.86)
- Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering (ICLR 2025 · similarity 0.85)
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