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This paper proposes a movable image-based rendering (M-IBR) system for improving the viewing freedom and environmental modeling capability of conventional static IBR systems. The system supports object-based rendering and 3-D reconstruction capability and consists of three main components. 1) An improved video stabilization method to reduce the shaky motion frequently encountered in movable IBR systems. It employs local polynomial regression (LPR) to automatically select an appropriate bandwidth for smoothing the estimated motion. 2) A novel view synthesis algorithm using a new segmentation and mutual-information (MI)-based algorithm for dense depth map estimation, which relies on segmentation, LPRbased depth map smoothing, and MI-based matching algorithm to iteratively estimate the depth map. The method is very flexible and both semiautomatic and automatic segmentation methods can be employed. They rank fourth and sixth, respectively, in the Middlebury comparison of existing depth estimation methods. This allows high-quality renderings of outdoor scenes with improved mobility/freedom to be obtained. 3) A new 3-D reconstruction algorithm that utilizes the sequential structure-from-motion technique and the dense depth maps estimated previously. It relies on a new iterative point cloud refinement algorithm based on Kalman filter for outlier removal and the segmentation-MI-based algorithm to further refine the correspondences and the projection matrices. The mobility of our system allows us to recover more conveniently 3-D model of static objects from the improved point cloud using a new robust radial basis function-based modeling algorithm to further suppress possible outliers and generate smooth 3-D meshes of objects. Experimental results show that the proposed 3-D reconstruction algorithm significantly reduces the adverse effect of the outliers and produces high-quality renderings using shadow light field and the model reconstructed.