Hyperspectral LiDAR enables non-contact mapping of the 3D surface geometry of an object along with its spectral reflectance signature and has proved to be effective for automated point cloud segmentation in various remote sensing applications. The established hyperspectral LiDAR methods offer a range precision of a few mm to a few cm for distances exceeding several meters. We propose a novel approach to hyperspectral LiDAR scanning based on a supercontinuum (SC) coherently broadened from a 780 nm frequency comb. It provides high precision distance measurements along with target reflectance over the 570-970 nm range of the SC output. The distance measurements are carried out by monitoring the differential phase delay of intermode beat notes generated by direct photodetection, while the backscattered light spectrum is acquired using a commercial CCD spectrometer with 0.16 nm resolution across the 400 nm bandwidth of the SC output. We demonstrate a measurement precision below 0.1 mm for a stand-off range up to 50 m on a diffuse target with around 89% reflectance. The measured relative accuracy as compared to a reference interferometer is on the order of 10-5 for distances up to 50 m. Initial results also indicate spectrum-based material classification within a 3D point cloud using a linear support vector machine. The results highlight the potential of this approach for joint high-precision laser scanning and automated material classification.