Update README.md

Adding citation in Readme.md

README.md

@@ -1,48 +1,66 @@
----
-license: cc-by-nc-sa-4.0
----
-
-# ToF-360 Dataset  
-
-![Figure showing multiple modalities](assets/figure/figure_1.png?raw=true)
-
-## Overview  
-The ToF-360 dataset consists of spherical RGB-D images with instance-level semantic and room layout annotations, which include 4 unique scenes. It contains 179 equirectangular RGB images along with the corresponding depths, surface normals, XYZ images, and HHA images, labeled with building-defining object categories and image based layout boundaries (ceiling-wall, wall-floor). The dataset enables development of scene understanding tasks based on single-shot reconstruction without the need for global alignment in indoor spaces.
-
-## Dataset Modalities  
-Each scenes in the dataset has its own folder in the dataset. All the modalities for each area are contained in that folder as `<scene>/<modality>`.
-
-**RGB images:**  
-RGB images contain equirectangular 24-bit color and it is converted from raw dual fisheye image taken by a sensor.
-
-**Manhattan aligned RGB images:**  
-We followed the preprocessing code proposed by [[LGT-Net]](https://github.com/zhigangjiang/LGT-Net) to create Manhattan aligned RGB images. Sample code for our dataset is in `assets/preprocessing/align_manhattan.py`.
-
-**depth:**  
-Depth images are stored as 16-bit grayscale PNGs having a maximum depth of 128m and a sensitivity of 1/512m. Missing values are encoded with the value 0. Note that while depth is defined as the distance from the point-center of the camera in the panoramics. 
-
-**XYZ images:**  
-XYZ images are saved as `.npy` binary file format in [NumPy](https://numpy.org/). It contains pixel-aligned set of data points in space with a sensitivity of mm. It must be the size of (Height, Width, 3[xyz]).
-
-**Normal images:**  
-Normals are 127.5-centered per-channel surface normal images. The normal vector is saved as 24-bit RGB PNGs where Red is the horizontal value (more red to the right), Green is vertical (more green downwards), and Blue is towards the camera. It is computed by [normal estimation function](https://www.open3d.org/docs/0.7.0/python_api/open3d.geometry.estimate_normals.html) in [Open3D](https://github.com/isl-org/Open3D). The tool for creating normal images from 3D is located in the `assets/preprocessing/depth2normal.py`.
-
-**HHA images:**  
-HHA images contains horizontal disparity, height above ground and angle with gravity, respectively.
-We followed [Depth2HHA-python](https://github.com/charlesCXK/Depth2HHA-python) to create it. Code is located in `assets/preprocessing/getHHA.py`.
-
-**Annotation:**  
-We used the [COCO Annotator](https://github.com/jsbroks/coco-annotator) for labelling the RGB data. We follow [ontology-based annotation guidelines](https://www.dfki.de/fileadmin/user_upload/import/13246_EC3_2023_Ontology_based_annotation_of_RGB_D_images_and_point_clouds_for_a_domain_adapted_dataset.pdf) developed for both RGB-D and point cloud data.  
-`<scenes>/annotation` contains json format files, `<scenes>/semantics` and `<scenes>/instances>` have image-like labeled data stored as `.npy` binary file.
-
-**Room layout annotation:**  
-Room layout annotations are stored as same json format as [PanoAnnotator](https://github.com/SunDaDenny/PanoAnnotator). Please refer to this repo for more details.
-
-## Tools  
-This repository provides some basic tools for getting preprocessed data and evaluating dataset. The tools are located in the `assets/` folder.
-
-## Croissant metadata
-You can use [this instruction](https://huggingface.co/docs/datasets-server/croissant) provided by HuggingFace. `croissant_metadata.json` is also available.
-
-## Citations  
-Coming soon...
+---
+license: cc-by-nc-sa-4.0
+---
+
+# ToF-360 Dataset  
+
+![Figure showing multiple modalities](assets/figure/figure_1.png?raw=true)
+
+## Overview  
+The ToF-360 dataset consists of spherical RGB-D images with instance-level semantic and room layout annotations, which include 4 unique scenes. It contains 179 equirectangular RGB images along with the corresponding depths, surface normals, XYZ images, and HHA images, labeled with building-defining object categories and image based layout boundaries (ceiling-wall, wall-floor). The dataset enables development of scene understanding tasks based on single-shot reconstruction without the need for global alignment in indoor spaces.
+You can also find the paper [here](https://av.dfki.de/publications/tof-360-a-panoramic-time-of-flight-rgb-d-dataset-for-single-capture-indoor-semantic-3d-reconstruction/).
+
+## Dataset Modalities  
+Each scenes in the dataset has its own folder in the dataset. All the modalities for each area are contained in that folder as `<scene>/<modality>`.
+
+**RGB images:**  
+RGB images contain equirectangular 24-bit color and it is converted from raw dual fisheye image taken by a sensor.
+
+**Manhattan aligned RGB images:**  
+We followed the preprocessing code proposed by [[LGT-Net]](https://github.com/zhigangjiang/LGT-Net) to create Manhattan aligned RGB images. Sample code for our dataset is in `assets/preprocessing/align_manhattan.py`.
+
+**depth:**  
+Depth images are stored as 16-bit grayscale PNGs having a maximum depth of 128m and a sensitivity of 1/512m. Missing values are encoded with the value 0. Note that while depth is defined as the distance from the point-center of the camera in the panoramics. 
+
+**XYZ images:**  
+XYZ images are saved as `.npy` binary file format in [NumPy](https://numpy.org/). It contains pixel-aligned set of data points in space with a sensitivity of mm. It must be the size of (Height, Width, 3[xyz]).
+
+**Normal images:**  
+Normals are 127.5-centered per-channel surface normal images. The normal vector is saved as 24-bit RGB PNGs where Red is the horizontal value (more red to the right), Green is vertical (more green downwards), and Blue is towards the camera. It is computed by [normal estimation function](https://www.open3d.org/docs/0.7.0/python_api/open3d.geometry.estimate_normals.html) in [Open3D](https://github.com/isl-org/Open3D). The tool for creating normal images from 3D is located in the `assets/preprocessing/depth2normal.py`.
+
+**HHA images:**  
+HHA images contains horizontal disparity, height above ground and angle with gravity, respectively.
+We followed [Depth2HHA-python](https://github.com/charlesCXK/Depth2HHA-python) to create it. Code is located in `assets/preprocessing/getHHA.py`.
+
+**Annotation:**  
+We used the [COCO Annotator](https://github.com/jsbroks/coco-annotator) for labelling the RGB data. We follow [ontology-based annotation guidelines](https://www.dfki.de/fileadmin/user_upload/import/13246_EC3_2023_Ontology_based_annotation_of_RGB_D_images_and_point_clouds_for_a_domain_adapted_dataset.pdf) developed for both RGB-D and point cloud data.  
+`<scenes>/annotation` contains json format files, `<scenes>/semantics` and `<scenes>/instances>` have image-like labeled data stored as `.npy` binary file.
+
+**Room layout annotation:**  
+Room layout annotations are stored as same json format as [PanoAnnotator](https://github.com/SunDaDenny/PanoAnnotator). Please refer to this repo for more details.
+
+## Tools  
+This repository provides some basic tools for getting preprocessed data and evaluating dataset. The tools are located in the `assets/` folder.
+
+## Croissant metadata
+You can use [this instruction](https://huggingface.co/docs/datasets-server/croissant) provided by HuggingFace. `croissant_metadata.json` is also available.
+
+## Citations  
+If you use this code or dataset in your research, please cite the following paper:
+@inproceedings{pub15783,
+  author = {
+    Hideaki Kanayama and
+    Mahdi Chamseddine and
+    Suresh Guttikonda and
+    So Okumura and
+    Soichiro Yokota and
+    Didier Stricker and
+    Jason Raphael Rambach
+  },
+  title = {ToF-360 – A Panoramic Time-of-flight RGB-D Dataset for Single Capture Indoor Semantic 3D Reconstruction},
+  booktitle = {21st CVPR Workshop on Perception Beyond the Visible Spectrum (PBVS-2025)},
+  year = {2025},
+  publisher = {IEEE},
+  address = {Nashville, Tennessee, USA},
+  month = {June}
+}