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RGB-D Semantic Segmentation
3D Semantic Segmentation
Common
|...
License: Unknown

Overview

This page provides a RGB-D Segmentation dataset with groundtruth acquired with a Microsoft
Kinect
device. The dataset includes 5 (+ 1 for the background) categories of common grocery
products such as packets of biscuits, juice bottles, coffee cans and boxes of salt, of different
brands and colors. The training set includes 3 model views for each category, while the testing
scenes are 16, including a high degree of clutter and occlusions. Thanks to the deployed device,
this dataset includes both color and depth. It also includes ground-truth, i.e. the correct
label to be assigned to each point of the test set.

Instruction

The dataset is structured into two main folders:

Training set:

Each model of each of the 6 categories includes 3 files:

  • .yaml: this file stores 4 arrays in the OpenCV "CvMat"
    format and represents the 640x480 RGB-D range map. The first 3 float arrays (named "X", "Y",
    "Z") are reserved for the depth (x,y,z values), while the 4th array (named "T") is a 3-channel
    IplImage that stores the RGB texture information. We suggest to load it by means of the cvLoad
    function included in the OpenCV library.
  • .ply: the 3D
    mesh; can be viewed by means of , e.g., MeshLab.
  • .png: the RGB image

Test set:

Each of the 16 test scenes includes the following files (XX is a number from 0 to 15):

  • SceneXX.yaml: the RGB-D range map - see above.
  • SceneXX.ply: the 3D mesh
  • SceneXX_2D.png: the RGB image
  • SceneXX_GT2D.XYZ_LABEL_CONF:
    the groundtruth labels, referred to each point of the 2D arrays of the range map
  • SceneXX_GT.XYZ_LABEL_CONF:
    the groundtruth labels, referred to each (x,y,z) value of the 3D mesh.
  • SceneXX_GT3D.ply:
    the scene 3D mesh where the original texture has been substituted by the groundtruth labels.
    Useful to visualize the groundtruth.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{tombari2011online,
  title={Online learning for automatic segmentation of 3D data},
  author={Tombari, Federico and Di Stefano, Luigi and Giardino, Simone},
  booktitle={2011 IEEE/RSJ international conference on Intelligent Robots and Systems},
  pages={4857--4864},
  year={2011},
  organization={IEEE}
}
Data Summary
Type
Image,
Amount
--
Size
353.88MB
| Amount -- | Size 353.88MB
RGB-D Semantic Segmentation
3D Semantic Segmentation
Common
License: Unknown

Overview

This page provides a RGB-D Segmentation dataset with groundtruth acquired with a Microsoft
Kinect
device. The dataset includes 5 (+ 1 for the background) categories of common grocery
products such as packets of biscuits, juice bottles, coffee cans and boxes of salt, of different
brands and colors. The training set includes 3 model views for each category, while the testing
scenes are 16, including a high degree of clutter and occlusions. Thanks to the deployed device,
this dataset includes both color and depth. It also includes ground-truth, i.e. the correct
label to be assigned to each point of the test set.

Instruction

The dataset is structured into two main folders:

Training set:

Each model of each of the 6 categories includes 3 files:

  • .yaml: this file stores 4 arrays in the OpenCV "CvMat"
    format and represents the 640x480 RGB-D range map. The first 3 float arrays (named "X", "Y",
    "Z") are reserved for the depth (x,y,z values), while the 4th array (named "T") is a 3-channel
    IplImage that stores the RGB texture information. We suggest to load it by means of the cvLoad
    function included in the OpenCV library.
  • .ply: the 3D
    mesh; can be viewed by means of , e.g., MeshLab.
  • .png: the RGB image

Test set:

Each of the 16 test scenes includes the following files (XX is a number from 0 to 15):

  • SceneXX.yaml: the RGB-D range map - see above.
  • SceneXX.ply: the 3D mesh
  • SceneXX_2D.png: the RGB image
  • SceneXX_GT2D.XYZ_LABEL_CONF:
    the groundtruth labels, referred to each point of the 2D arrays of the range map
  • SceneXX_GT.XYZ_LABEL_CONF:
    the groundtruth labels, referred to each (x,y,z) value of the 3D mesh.
  • SceneXX_GT3D.ply:
    the scene 3D mesh where the original texture has been substituted by the groundtruth labels.
    Useful to visualize the groundtruth.

Citation

Please use the following citation when referencing the dataset:

@inproceedings{tombari2011online,
  title={Online learning for automatic segmentation of 3D data},
  author={Tombari, Federico and Di Stefano, Luigi and Giardino, Simone},
  booktitle={2011 IEEE/RSJ international conference on Intelligent Robots and Systems},
  pages={4857--4864},
  year={2011},
  organization={IEEE}
}
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