Surgical guidance and decision making could be improved with accurate measurement of intra-operative data including shape and hyper/multispectral information of the tissue surface. In this thesis, a dual-modality endoscopic system based on the structured light (SL) technique has been proposed to enable real-time tissue surface reconstruction and pixel-level dense multispectral imaging. The system is described in a chronological order of development for both the hardware and software aspects. Starting from the previously developed ICL SL system that enabled tissue surface shape measurement, a naive 3D reconstruction pipeline is described. This includes a SL system calibration method, normalized cut, and local rigid registration based pattern decoding algorithm. In order to enable a faster, denser, and more robust surface reconstruction, further hardware and algorithmic improvements have been proposed, including replacing the RGB camera with a multispectral camera and stroboscopically switching between white light (WL)/SL modes. Software improvements have allowed on-the-fly calibration and reconstruction, and improved accuracy through the integration of local normal information, the use of convolutional neural networks (CNN) based pattern decoding with a specially designed feature matching algorithm, and the combination of SL and structure-from-motion (SfM). The system has also been extended to enable sparse hyperspectral signal measurements, by integrating a slit hyperspectral camera. A CNN based model has been developed to estimate pixel-level dense multispectral images from RGB images and the captured sparse hyperspectral signals. The whole system has been validated using phantoms and ex vivo tissue, with in vivo demonstrations in a large animal trial and in patients, demonstrating its potential in real-world clinical applications.