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.

翻译：可见图像被广泛用于运动估计。相形之下,热图像更难用于运动估计,因为这些图像一般分辨率较低、纹理较少、噪音更多。在本文中,展示了用于评价多光谱运动估计系统性能的新数据集。所有序列都从手持多光谱设备记录下来。由标准可见光相机、长波红外摄像头、RGB-D照相机和惯性测量单位组成。多光谱图像,包括全传感器分辨率(640x480)的色和热图像(640x480),都是从32赫兹有硬件同步的标准和长波红外摄像头获得的。深度图像由微软 Kinect2 捕获,可以用于学习跨模式立声匹配。在轨迹评估中,提供了从运动捕捉取系统获得的准确的地面图象摄像器。除了有明亮照明的序列外,数据集还包含模糊、不同和复杂的照明场景。完整的数据集,包括有详细数据规格的原始数据和校准数据格式。