17 lines
1.3 KiB
Markdown
17 lines
1.3 KiB
Markdown
# CSE5519 Advances in Computer Vision (Topic G: 2023: Correspondence Estimation and Structure from Motion)
|
|
|
|
## Detector-Free Structure from Motion
|
|
|
|
[link to paper](https://arxiv.org/abs/2306.15669)
|
|
|
|
- A new detector-free SfM framework built upon detector-free matchers to handle texture-pool scenes.
|
|
- An iterative refinement pipeline with a transformer-based multi-view matching network to efficiently refine both feature tracks and reconstruction results.
|
|
- Multi-view Feature transformer to enhance the discrimitiveness of extracted features.
|
|
- Use Bundle adjustment (view point consistency)
|
|
- Use Topology adjustment (merge, complete, or remove vertices using pre defined rules)
|
|
|
|
> [!TIP]
|
|
>
|
|
> This paper proposed a new detector-free SfM framework built upon detector-free matchers to handle texture-pool scenes and use an iterative refinement pipeline with a transformer-based multi-view matching network to efficiently refine both feature tracks and reconstruction results.
|
|
>
|
|
> I'm particularly interested in the detector-free matchers and the transformer-based multi-view matching network. Due to time constraints, I don't have much time to check the work for detector-free matchers and how they generate the coarse model for predicted matches. I'm looking forward to hearing more about this topic in tomorrow's presentation. |