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README.md

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-license: cc-by-nc-sa-4.0
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-
-# 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 ayout 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 and metadata for each area are contained in that folder as `<scene>/<modality>`.
-
-**HHA images:**  
-We followed [[Depth2HHA-python]](https://github.com/charlesCXK/Depth2HHA-python) to create it.
-
-**RGB images:**  
-RGB images contain equirectangular 24-bit color images converted from raw dual fisheye image. 
-
-**Manhattan aligned RGB images:**  
-We followed [[LGT-Net]](https://github.com/zhigangjiang/LGT-Net) to create Manhattan aligned RGB images.
-
-**XYZ images:**  
-XYZ images are saved as `.npy` binary file format in NumPy. 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]).
-
-**Annotation:**  
-
-**depth:**  
-Depth images are stored as 16-bit 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. 
-
-**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.
-
-**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/compute_normal.py`
-
-## Tools  
-This repository provides some basic tools for interacting with the dataset and how to get preprocessed data. The tools are located in the `assets/preprocessing` folder.
-
-## Evaluation  
-**Semantic segmentation (image-based):**  
-
-
-**Semantic segmentation (pointcloud-based):**  
-
-
-**Layout estimation:**  
-
-
-## Citations  
+---
+license: cc-by-nc-sa-4.0
+---
+
+# ToF-360 Dataset  (test)
+
+![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 ayout 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 and metadata for each area are contained in that folder as `<scene>/<modality>`.
+
+**HHA images:**  
+We followed [[Depth2HHA-python]](https://github.com/charlesCXK/Depth2HHA-python) to create it.
+
+**RGB images:**  
+RGB images contain equirectangular 24-bit color images converted from raw dual fisheye image. 
+
+**Manhattan aligned RGB images:**  
+We followed [[LGT-Net]](https://github.com/zhigangjiang/LGT-Net) to create Manhattan aligned RGB images.
+
+**XYZ images:**  
+XYZ images are saved as `.npy` binary file format in NumPy. 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]).
+
+**Annotation:**  
+
+**depth:**  
+Depth images are stored as 16-bit 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. 
+
+**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.
+
+**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/compute_normal.py`
+
+## Tools  
+This repository provides some basic tools for interacting with the dataset and how to get preprocessed data. The tools are located in the `assets/preprocessing` folder.
+
+## Evaluation  
+**Semantic segmentation (image-based):**  
+
+
+**Semantic segmentation (pointcloud-based):**  
+
+
+**Layout estimation:**  
+
+
+## Citations  
 Coming soon...