Three-dimensional (3D) reconstruction serves as a cornerstone in various robotic applications, playing critical roles in scene understanding and navigation. Traditionally, LiDAR has been instrumental in generating precise point clouds of the environment, providing essential data for these applications. However, the efficacy of LiDAR sensors is significantly hindered in challenging conditions, such as the presence of water or icy surfaces. The complex interplay between laser beams and icy or non-ideal surfaces can result in signal degradation, distortion, or even complete signal loss, adversely affecting the accuracy and reliability of the 3D reconstruction process. The reflective and refractive properties of ice, along with its variable surface conditions, present challenges that traditional LiDAR sensors struggle to address. This paper proposes a diverse dataset to facilitate a multimodal approach for detecting and reconstructing icy surfaces using various sensors. A preliminary study on our dataset demonstrates that, in addition to the geometrical surface obtained by registering consecutive scans from LiDAR, regions with ice can be identified by leveraging visual data to enhance understanding of the surface texture. The integration of distinct data sources can thus improve the robustness of reconstruction algorithms in diverse scenarios.