add arXiv link

README.md

@@ -9,12 +9,13 @@ Official implementation of the paper
 
 > **Rethinking Inductive Biases for Surface Normal Estimation**
 >
-> CVPR 2024 (to appear)
+> CVPR 2024
 >
 > <a href="https://baegwangbin.com" target="_blank">Gwangbin Bae</a> and <a href="https://www.doc.ic.ac.uk/~ajd/" target="_blank">Andrew J. Davison</a>
 >
 > <a href="https://github.com/baegwangbin/DSINE/raw/main/paper.pdf" target="_blank">[paper.pdf]</a>
-<a href="https://arxiv.org/" target="_blank">[arXiv (coming soon)]</a> 
+<a href="https://arxiv.org/abs/2403.00712" target="_blank">[arXiv]</a> 
+<a href="https://www.youtube.com/watch?v=2y9-35c719Y&t=5s" target="_blank">[youtube]</a> 
 <a href="https://baegwangbin.github.io/DSINE/" target="_blank">[project page]</a>
 
 ## Abstract
@@ -43,4 +44,17 @@ Then, download the model weights from <a href="https://drive.google.com/drive/fo
 ## Test on images
 
 * Run `python test.py` to generate predictions for the images under `./samples/img/`. The result will be saved under `./samples/output/`.
-* Our model assumes known camera intrinsics, but providing approximate intrinsics still gives good results. For some images in `./samples/img/`, the corresponding camera intrinsics (fx, fy, cx, cy - assuming perspective camera with no distortion) is provided as a `.txt` file. If such a file does not exist, the intrinsics will be approximated, by assuming $60^\circ$ field-of-view.
+* Our model assumes known camera intrinsics, but providing approximate intrinsics still gives good results. For some images in `./samples/img/`, the corresponding camera intrinsics (fx, fy, cx, cy - assuming perspective camera with no distortion) is provided as a `.txt` file. If such a file does not exist, the intrinsics will be approximated, by assuming $60^\circ$ field-of-view.
+
+## Citation
+
+If you find our work useful in your research please consider citing our paper:
+
+```
+@inproceedings{bae2024dsine,
+    title={Rethinking Inductive Biases for Surface Normal Estimation},
+    author={Gwangbin Bae and Andrew J. Davison},
+    booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year={2024}
+}
+```

docs/index.html

@@ -79,9 +79,9 @@ <h1 class="title is-2 is-size-3-mobile is-spaced has-text-centered">
                 <span class="icon"><i class="fas fa-file-pdf"></i></span>
                 <span>Paper</span>
             </a>
-            <a href="https://arxiv.org/" class="button is-rounded is-link is-light mr-2" target="_blank" rel="noopener noreferrer">
+            <a href="https://arxiv.org/abs/2403.00712" class="button is-rounded is-link is-light mr-2" target="_blank" rel="noopener noreferrer">
                 <span class="icon"><i class="ai ai-arxiv"></i></span>
-                <span>arXiv (coming soon)</span>
+                <span>arXiv</span>
             </a>
             <a href="https://github.com/baegwangbin/DSINE" class="button is-rounded is-link is-light" target="_blank" rel="noopener noreferrer">
                 <span class="icon"><i class="fab fa-github"></i></span>