Conventionally, human intuition defines vision as a modality of passive optical sensing, relying on ambient light to perceive the environment. However, active optical sensing, which involves emitting and receiving signals, offers unique advantages by capturing both radiometric and geometric properties of the environment, independent of external illumination conditions. This work focuses on advancing active optical sensing using Light Detection and Ranging (LiDAR), which captures intensity data, enabling the estimation of surface reflectance that remains invariant under varying illumination. Such properties are crucial for robotic perception tasks, including detection, recognition, segmentation, and Simultaneous Localization and Mapping (SLAM). A key challenge with low-cost LiDARs lies in the sparsity of scan data, which limits their broader application. To address this limitation, this work introduces an innovative framework for generating dense LiDAR intensity images from sparse data, leveraging the unique attributes of non-repeating scanning LiDAR (NRS-LiDAR). We tackle critical challenges, including intensity calibration and the transition from static to dynamic scene domains, facilitating the reconstruction of dense intensity images in real-world settings. The key contributions of this work include a comprehensive dataset for LiDAR intensity image densification, a densification network tailored for NRS-LiDAR, and diverse applications such as loop closure and traffic lane detection using the generated dense intensity images. Experimental results validate the efficacy of the proposed approach, which successfully integrates computer vision techniques with LiDAR data processing, enhancing the applicability of low-cost LiDAR systems and establishing a novel paradigm for robotic vision via active optical sensing--LiDAR as a Camera.