Summary of Mutli-view 3d Reconstruction Using Knowledge Distillation, by Aditya Dutt et al.
Mutli-View 3D Reconstruction using Knowledge Distillation
by Aditya Dutt, Ishikaa Lunawat, Manpreet Kaur
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed pipeline utilizes knowledge distillation to develop a student-teacher model with Dust3r as the teacher, aiming to build student models that can learn scene-specific representations and output 3D points with replicable performance. The approach involves training multiple architectures of student models using 3D reconstructed points from Dust3r, with CNN-based and Vision Transformer-based models being tested. Pre-trained models are compared against those built from scratch, showcasing the effectiveness of the Vision Transformer in achieving best visual and quantitative performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make large foundation models like Dust3r more accessible by training smaller student models that can achieve similar results with less computation required. The team used 12Scenes dataset to train their models and tested two main architectures: CNN-based and Vision Transformer-based. They found that the Vision Transformer performed best, both visually and quantitatively. This means that in the future, we may be able to use smaller models for tasks like Visual Localization without sacrificing too much accuracy. |
Keywords
» Artificial intelligence » Cnn » Knowledge distillation » Teacher model » Vision transformer
