In this paper, we propose a robust algorithm for 3D object pose estimation from a single 2D image. The proposed pose estimation algorithm is based on modifying the traditional image projection error function to a sum of squared image projection errors weighted by their associated distances. By using an Euler angle representation, we formulate the energy minimization for the pose estimation problem as searching a global minimum solution. Based on this framework, the proposed algorithm employs robust techniques to detect outliers in a coarse-to-fine fashion, thus providing very robust pose estimation. Our experiments show that the algorithm outperforms previous methods under noisy conditions.
In this paper, we propose a novel approach to reconstructing depth map from a video sequence, which not only considers geometry coherence but also temporal coherence. Most of the previous methods of reconstructing depth map from video are based on the assumption of rigid motion, thus they cannot provide satisfactory depth estimation for regions with moving objects. In this work, we develop a depth estimation algorithm that detects regions of moving objects and recover the depth map in a Markov Random Field framework. We first apply SIFT matching across frames in the video sequence and compute the camera parameters for all frames and the 3D positions of the SIFT feature points via structure from motion. Then, the 3D depths at these SIFT points are propagated to the whole image based on image over-segmentation to construct an initial depth map. Then the depth values for the segments with large reprojection errors are refined by minimizing the corresponding re-projection errors. In addition, we detect the area of moving objects from the remaining pixels with large re-projection errors. In the final step, we optimize the depth map estimation in a Markov random filed framework. Some experimental results are shown to demonstrate improved depth estimation results of the proposed algorithm.
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