  Visible images have been widely used for motion estimation. Thermal images,
in contrast, are more challenging to be used in motion estimation since they
typically have lower resolution, less texture, and more noise. In this paper, a
novel dataset for evaluating the performance of multi-spectral motion
estimation systems is presented. All the sequences are recorded from a handheld
multi-spectral device. It consists of a standard visible-light camera, a
long-wave infrared camera, an RGB-D camera, and an inertial measurement unit
(IMU). The multi-spectral images, including both color and thermal images in
full sensor resolution (640 x 480), are obtained from a standard and a
long-wave infrared camera at 32Hz with hardware-synchronization. The depth
images are captured by a Microsoft Kinect2 and can have benefits for learning
cross-modalities stereo matching. For trajectory evaluation, accurate
ground-truth camera poses obtained from a motion capture system are provided.
In addition to the sequences with bright illumination, the dataset also
contains dim, varying, and complex illumination scenes. The full dataset,
including raw data and calibration data with detailed data format
specifications, is publicly available.
