Skip to main content

Dense Reconstruction Dataset

Dense Reconstruction Dataset

In practice, we found dense reconstructions are the most complete and highest quality (with our mobile robotics platform) when fusing data from multiple Velodyne, SICK LMS- 151, and stereo cameras using our own datasets. We are releasing such a dataset to provide a realistic mobile-robotics platform with a variety of sensors—a few of which are prime candidates for ground truth when testing the accuracy of reconstructions with other sensors. For example, the push-broom laser sensor (which excels at 3D urban reconstructions) can be used to compare the reconstruction quality of monocular vs. stereo camera vs. Velodyne vs. a combination thereof.

This dataset is ideal to benchmark and evaluate large- scale dense reconstruction frameworks. It was collected in Oxford, UK at mid-day, thus it provides a representative urban environment with numerous pedestrians, bicycles, and vehicles visible to all sensors throughout the 1.6 km trajectory.

It includes data from the following sensors which collectively provide a continuous 360◦ view around the vehicle:

  • 1x Point Grey Bumblebee XB3 Stereo Camera (Color)
  • 1x Point Grey Bumblebee2 Stereo Camera (Grayscale)
  • 4x Point Grey Grasshopper2 Monocular Cameras (Color, Fisheye Lens)
  • 2x Velodyne HDL-32E 3D lidars
  • 3x SICK LMS-151 2D lidars

In addition, the following is provided to aid in processing the raw sensor data:

  • Optimized SE(3) vehicle trajectory
  • Undistorted, rectified stereo image pairs
  • Undistorted mono images
  • Camera intrinsic
  • Extrinsic SE(3) transforms for all sensors

Finally, we provide example depth maps — using the techniques described in this paper — for the Bumblebee XB3 to enable users to rapidly utilize the dataset with their existing dense reconstruction pipelines. All data is stored in a similar format as KITTI along with MATLAB development toolkit.

To access to this dataset, contact us and request the ‘datastore1/mrgdatastore1/Logs/GEM/2015-02-03-11-52-03-broad-st-to-lab‘ dataset.