Research map / 3D vision / Face and shape
Learning Barycentric Representations of 3D Shapes for Sketch-Based 3D Shape Retrieval
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Aggregate multiple 3D views with learned Wasserstein barycenters instead of matching views independently | Sketch-based 3D shape retrieval · SHREC’13 sketch track | mAP: 0.469 -> 0.752 | Helped |
| Aggregate multiple 3D views with learned Wasserstein barycenters instead of matching views independently | Sketch-based 3D shape retrieval · SHREC’14 sketch track | mAP: 0.228 -> 0.401 | Helped |
| Use Wasserstein barycentric representation instead of max view-pooling (ablation) | Multi-view CNN retrieval on SHREC’14 · SHREC’14 sketch track | precision | Helped |
| Increase the rendered-view count from 3 to 4 (ablation) | LWBR · SHREC’14 sketch track | mAP: 0.325 -> 0.363 | Helped |
| Increase the rendered-view count from 4 to 6 (ablation) | LWBR · SHREC’14 sketch track | mAP: 0.363 -> 0.390 | Helped |
| Increase the rendered-view count from 6 to 12 (ablation) | LWBR · SHREC’14 sketch track | mAP: 0.390 -> 0.401 | Helped |
Closest papers by meaning
- Deep Correlated Metric Learning for Sketch-based 3D Shape Retrieval (AAAI 2017 · similarity 0.91)
- Deep Sketch-Shape Hashing With Segmented 3D Stochastic Viewing (CVPR 2019 · similarity 0.90)
- Learning Cross-Domain Neural Networks for Sketch-Based 3D Shape Retrieval (AAAI 2016 · similarity 0.89)
- MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval (AAAI 2019 · similarity 0.88)
- Doodle to Object: Practical Zero-Shot Sketch-Based 3D Shape Retrieval (AAAI 2023 · similarity 0.87)
- SketchINR: A First Look into Sketches as Implicit Neural Representations (CVPR 2024 · similarity 0.84)
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