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 # 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?