# CSE5519 Advances in Computer Vision (Topic E: 2024: Deep Learning for Geometric Computer Vision)

## DUSt3R: Geometric 3D Vision Made Easy.Links to an external site.

[link to paper](https://arxiv.org/pdf/2312.14132)

### Novelty in DUSt3R

Use point map to represent the 3D scene, combining with the camera intrinsics to estimate the 3D scene.

Direct-RGB to 3D scene.

Use ViT to encode the image, and then use two Transformer decoder (with information sharing between them) to decode the two representation of the same scene $F_1$ and $F_2$. Direct regression from RGB to point map and confidence map.

>[!TIP]
>
> Compared with previous works, this paper directly regresses the point map and confidence map from RGB, producing a more accurate and efficient 3D scene representation.
> 
> However, I'm not sure how the information across the two representations is shared in the Transformer decoder. If for a multiview image, there are two pairs of images that don't have any overlapping region, how can the model correctly reconstruct the 3D scene?
