Research map / 3D vision / Face and shape
Deep Correlated Metric Learning for Sketch-based 3D Shape Retrieval
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| DCML vs WCNN (previous best Siamese sketch-shape method) | sketch-based 3D shape retrieval · SHREC 2013 | mAP: 0.469 -> 0.674 | Helped |
| DCML vs WCNN | sketch-based 3D shape retrieval · SHREC 2014 | mAP: 0.228 -> 0.286 | Helped |
| DCML vs WCNN | sketch-based 3D shape retrieval · SHREC 2014 | DCG: 0.495 -> 0.498 | No clear effect |
| DCML at high recall vs prior methods | sketch-based 3D shape retrieval · SHREC 2014 | precision at recall > 0.5 | Hurt |
Closest papers by meaning
- Learning Barycentric Representations of 3D Shapes for Sketch-Based 3D Shape Retrieval (CVPR 2017 · similarity 0.91)
- Learning Cross-Domain Neural Networks for Sketch-Based 3D Shape Retrieval (AAAI 2016 · similarity 0.89)
- Deep Sketch-Shape Hashing With Segmented 3D Stochastic Viewing (CVPR 2019 · similarity 0.89)
- Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval (AAAI 2022 · similarity 0.88)
- Angular Triplet-Center Loss for Multi-View 3D Shape Retrieval (AAAI 2019 · similarity 0.87)
- KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation (CVPR 2024 · similarity 0.84)
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