license: cc-by-nc-sa-4.0
ToF-360 Dataset
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] 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. 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 in 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 to create it. Code is located in assets/preprocessing/getHHA.py
.
Annotation:
We used the COCO Annotator for labelling the RGB data. We follow ontology-based annotation guidelines 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. 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 provided by HuggingFace. croissant_metadata.json
is also available.
Citations
Coming soon...