The mobile mapping system (MMS) can acquire dense point-clouds of roads and roadside features. Roads are often separated into roadways and walkways in many urban areas. Since guardrails are installed to separate roadways and sidewalks, it is important to detect guardrails from point-clouds and reconstruct their 3D models for 3D street maps. Since there are a large variety of designs for guardrails in Japan, flexible methods are required for detection and reconstruction of guardrails. In this paper, we propose a new method for extracting guardrails from point-clouds, and reconstructing their 3D models. Since the MMS captures point-clouds and camera images synchronously, guardrails are detected using both point-clouds and images. In our method, point-clouds are segmented into small segments, and corresponding images are cropped from camera images. Then cropped images are classified into two classes of guardrails and others using the convolutional neural network. When guardrail points are obtained, 3D models of guardrails are reconstructed. However, point-clouds of guardrails are too sparse to reconstruct 3D shapes when guardrails consist of thin pipes. Since the same unit shape repeatedly appears in a guardrail, we create dense point-clouds by superimposing points of unit shapes. Then we reconstruct 3D shapes of pipes, beams, and poles of guardrails. In our evaluation using point-clouds in urban areas, our method could achieve good results of extraction and shape reconstruction of guardrails.